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This paper attempts to systematize natural language analysis process by (I) use of a partitioned semantic network formalism as the meaning representation and (2) stepwise translation based on Montague Grammar. …...

In this paper, we present a computational method for transforming a syntactic graph, which represents all syntactic interpretations of a sentence, into a semantic graph which filters out certain interpretations, …...

This paper discusses an information extraction (IE) system, Textract, in natural language (NL) question answering (QA) and examines the role of IE in QA application. It shows: (i) Named Entity …...

We present the technique of Virtual Annotation as a specialization of Predictive Annotation for answering definitional What is questions. These questions generally have the property that the type of the …...

This paper introduces a set of guidelines for annotating time expressions with a canonicalized representation of the times they refer to. Applications that can benefit from such an annotated corpus …...

This paper presents a novel language-independent question/answering (Q/A) system based on natural language processing techniques, shallow query understanding, dynamic sliding window techniques, and statistical proximity distribution matching techniques. The per- …...

We describe the treatment of questions (Question-Answer Typology, question parsing, and results) in the Weblcopedia question answering system. 1....

While Named Entity extraction is useful in many natural language applications, the coarse categories that most NE extractors work with prove insufficient for complex applications such as Question Answering and …...

Recent TREC results have demonstrated the need for deeper text understanding methods. This paper introduces the idea of automated reasoning applied to question answering and shows the feasibility of integrating …...

Even though the question answering (QA) field appeared only in recent years, there are systems for English which obtain good results for open- domain questions. The situation is very different …...

The discovery of semantic relations from text becomes increasingly important for applica- tions such as Question Answering, Informa- tion Extraction, Text Summarization, Text Un- derstanding, and others. The semantic rela- …...

Recent work in Question Answering has focused on web-based systems that extract answers using simple lexico- syntactic patterns. We present an alternative strategy in which patterns are used to extract …...

Today, corpus plays an important role in development and evaluation language and speech technologies, such as part of speech tagging, parsing, word sense disambiguation, text categorization, named entity classification, information …...

The field of Psychometrics routinely grapples with the question of what it means to measure the inherent ability of an organism to perform a given task, and for the last …...

This paper presents an approach on a query- biased document summarisation and an evaluation of the use of such an approach for question answering tasks. We observed a significant difference …...

This paper presents a method to construct Japanese KATAKANA variant list from large corpus. Our method is useful for information retrieval, information extrac- tion, question answering, and so on, because …...

Discovering the significant relations embedded in documents would be very useful not only for infor- mation retrieval but also for question answering and summarization. Prior methods for relation discov- ery, …...

Anticipating the availability of large question- answer datasets, we propose a principled, data- driven Instance-Based approach to Question An- swering. Most question answering systems incor- porate three major steps: classify …...

Accurate dependency recovery has recently been reported for a number of wide-coverage statistical parsers using Combinatory Categorial Grammar (CCG). However, overall figures give no indication of a parser’s performance on …...

A given entity, representing a person, a location or an organization, may be mentioned in text in multiple, ambiguous ways. Understanding natural language requires identifying whether different mentions of a …...

This paper presents a multi-layered Question An- swering (Q.A.) architecture suitable for enhanc- ing current Q.A. capabilities with the possibility of processing complex questions. That is, questions whose answer needs …...

While research on question answering has be- come popular in recent years, the problem of ef- ficiently locating a complete set of distinct answers to list questions in huge corpora …...

Recently, shallow parsing has been applied to various information processing systems, such as information retrieval, information extraction, question answering, and automatic document summarization. A shallow parser is suitable for online …...

This paper presents a hybrid approach to question answering in the clinical domain that combines techniques from summarization and information retrieval. We tackle a frequently-occurring class of questions that takes …...

. We propose an information retrieval method based on question identification. We have implemented and integrated a prototype Chinese speech IR system for a commercial PDA with this method for …...

NLP systems for tasks such as question answering and information extraction typ- ically rely on statistical parsers. But the ef- ficacy of such parsers can be surprisingly low, particularly for …...

Complex tasks like question answering need to be able to identify events in text and the relations among those events. We show that this event identification task and a related …...

Work on the semantics of questions has argued that the relation between a ques- tion and its answer(s) can be cast in terms of logical entailment. In this paper, we …...

Relevance feedback has already proven its usefulness in probabilistic information re- trieval (IR). In this research we explore whether a pseudo relevance feedback tech- nique on IR can improve the …...

The end-to-end performance of natural language processing systems for com- pound tasks, such as question answering and textual entailment, is often hampered by use of a greedy 1-best pipeline archi- …...

Semantic inference is a key component for advanced natural language under- standing. However, existing collections of automatically acquired inference rules have shown disappointing results when used in applications such as …...

Many systems for tasks such as question answer- ing, multi-document summarization, and infor- mation retrieval need robust numerical measures of lexical relatedness. Standard thesaurus-based measures of word pair similarity are …...

One of the main challenges in the applica- tions (i.e.: text summarization, question an- swering, information retrieval, etc.) of Natural Language Processing is to deter- mine which of the several …...

We present a user requirements study for Question Answering on meeting records that assesses the difficulty of users ques- tions in terms of what type of knowledge is required in …...

The limitations of existing data sets for train- ing parsers has led to a need for additional data. However, the cost of manually annotat- ing the amount and range of …...

Chinese is a language that does not have mor- phological tense markers that provide explicit grammaticalization of the temporal location of situations (events or states). However, in many NLP applications …...

This paper explores the use of set expan- sion (SE) to improve question answering (QA) when the expected answer is a list of entities belonging to a certain class. Given …...

Question answering research has only recently started to spread from short factoid questions to more complex ones. One significant chal- lenge is the evaluation: manual evaluation is a difficult, time-consuming …...

. We adopt a previously developed model of deep syntactic and semantic processing to support question answering for Bahasa Indonesia, and extend it by adding a number of axioms designed …...

. The automatic extraction of biographical information from business news is a com- plex task. This approach deals with the characterization of the so called biographical relations by means of …...

. This paper is a contribution to text semantics processing and its application to advanced question-answering where a significant portion of a well-formed text is required as a response. We …...

Online forum discussions often contain vast amounts of questions that are the focuses of discussions. Extracting contexts and answers together with the questions will yield not only a coherent forum …...

The development of the Web 2.0 led to the birth of new textual genres such as blogs, reviews or forum entries. The increasing number of such texts and the highly …...

. Verb classifications have been used to support a number of practical tasks and ap- plications, such as parsing, information extraction, question-answering, and machine trans- lation. However, large-scale exploitation of …...

Document Retrieval assumes that a document is independent of its relevance, and non-relevance. Previous works showed that the same assump- tion is being considered for passage retrieval in the context …...

In this paper, we introduce an automatic method for classifying a given question using broad semantic categories in an existing lexical database (i.e., WordNet) as the class tagset. For this, …...

This paper addresses question analysis in the framework of Question Answering over struc- tured data. The problem is set as a relation extraction task, where all the relations of inter- …...

AbstractMany natural language processing tasks, including information extraction, question answering and recognizing textual entailment, require analysis of the polarity, focus of polarity, tense, aspect, mood and source of the event …...

Determining whether two terms in text have an ancestor relation (e.g. Toyota and car) or a sibling relation (e.g. Toyota and Honda) is an essential component of textual inference in …...

AbstractThis paper focuses on the improvement of the conceptual structure of FrameNet (FN) for the sake of applying this resource to knowledge-intensive NLP tasks requiring reasoning, such as question answering, …...

AbstractThe paper offers an overview of the key issues raised during the seven years’ activity of the Multilingual Question Answering Track at the Cross Language Evaluation Forum (CLEF). The general …...

AbstractQuestion Answering (QA) technology aims at providing relevant answers to natural language questions. Most Question Answering research has focused on mining document collections containing written texts to answer written questions. …...

Quantifying the semantic relevance be- tween questions and their candidate an- swers is essential to answer detection in social media corpora. In this paper, a deep belief network is proposed …...

A range of Natural Language Process- ing tasks involve making judgments about the semantic relatedness of a pair of sen- tences, such as Recognizing Textual En- tailment (RTE) and answer …...

AbstractIn the QA and information retrieval domains progress has been assessed via evaluation campaigns(Clef, Ntcir, Equer, Trec).In these evaluations, the systems handle independent questions and should provide one answer to …...

AbstractAnalysts in various domains, especially intelligence and financial, have to constantly extract useful knowledge from large amounts of unstructured or semi-structured data. Keyword-based search, faceted search, question-answering, etc. are some …...

Extracting temporal information from raw text is fundamental for deep language understand- ing, and key to many applications like ques- tion answering, information extraction, and document summarization. In this paper, …...

In this paper we give an overview of the Knowledge Base Population (KBP) track at the 2010 Text Analysis Conference. The main goal of KBP is to promote research in …...

Usually, in the Question Answering do- main, for a question in natural language, precise answers to the question are ex- tracted from documents according only to the context of the …...

The ability to accurately determine temporal relations between events is an important task for several natural language processing applications such as Question Answering, Summarization, and Information Extraction. Since current supervised …...

The last decade has seen many interesting ap- plications of Question Answering (QA) tech- nology. The Jeopardy! quiz show is certainly one of the most fascinating, from the view- points …...

AbstractThe DBpedia project extracts structured information from Wikipedia editions in 97 different languages and combines this information into a large multi-lingual knowledge base covering many specific domains and general world …...

AbstractThis paper describes a methodology for testing and evaluating the performance of Machine Reading systems through Question Answering and Reading Comprehension Tests. The methodology is being used in QA4MRE (QA …...

In this paper, we introduce new features for question-answering systems. These features are inspired by the fact that justification of the correct answer (out of many candidate answers) may be …...

Subjectivity analysis has been actively used in various applications such as opinion mining of customer reviews in online review sites, question-answering in CQA sites, multi-document summarization, etc. However, there has …...

Techniques that compare short text seg- ments using dependency paths (or simply, paths) appear in a wide range of automated language processing applications including question answering (QA). However, few models …...

We study question answering as a ma- chine learning problem, and induce a func- tion that maps open-domain questions to queries over a database of web extrac- tions. Given a …...

We consider the challenge of learning seman- tic parsers that scale to large, open-domain problems, such as question answering with Freebase. In such settings, the sentences cover a wide variety …...

We introduce a novel discriminative model for phrase-based monolingual alignment using a semi-Markov CRF. Our model achieves state- of-the-art alignment accuracy on two phrase- based alignment datasets (RTE and para- …...

Web pages segmentation for document selection in Question Answering In this paper, we study two different kinds of web pages segmentation for document selection in question answering. The segmentation is …...

AbstractExhibiting inferential capabilities is one of the major goals of many modern Natural Language Processing systems. However, if attempts have been made to define what textual inferences are, few seek …...

Paraphrase generation is widely used for various natural language processing (NLP) applications such as question answering, multi-document summarization, and machine translation. In this study, we identify the problems occurring in …...

AbstractThis paper describes the data collection and annotation carried out within the DBOX project ( Eureka project, number E! 7152). This project aims to develop interactive games based on spoken …...

In this paper we study the task of movie script summarization, which we argue could enhance script browsing, give readers a rough idea of the script’s plotline, and speed up …...

Relational phrases (e.g., “got married to”) and their hypernyms (e.g., “is a relative of”) are central for many tasks including question answering, open information ex- traction, paraphrasing, and entailment de- …...

Monolingual alignment models have been shown to boost the performance of question answering systems by ”bridging the lexical chasm” between questions and answers. The main limitation of these approaches is …...

Recursive neural models, which use syn- tactic parse trees to recursively generate representations bottom-up, are a popular architecture. However there have not been rigorous evaluations showing for exactly which tasks …...

AbstractSeveral tasks in argumentation mining and debating, question-answering, and natural language inference involve classifying a sequence in the context of another sequence (referred as bi-sequence classification). For several single sequence …...

We show that a character-level encoder- decoder framework can be successfully ap- plied to question answering with a structured knowledge base. We use our model for single- relation question answering …...

In this paper, we study, compare and combine two state-of-the-art approaches to automatic feature engineering: Convolution Tree Ker- nels (CTKs) and Convolutional Neural Net- works (CNNs) for learning to rank …...

A common model for question answering (QA) is that a good answer is one that is closely related to the question, where re- latedness is often determined using general- purpose …...

AbstractRecent work for learning word representations has applied successfully to many NLP applications, such as sentiment analysis and question answering. However, most of these models assume a single vector per …...

To establish sophisticated dialogue sys- tems, text planning needs to cope with congruent as well as incongruent inter- locutor interests as given in everyday di- alogues. Little attention has been …...

AbstractRecently, end-to-end memory networks have shown promising results on Question Answering task, which encode the past facts into an explicit memory and perform reasoning ability by making multiple computational steps …...

AbstractEntailment recognition approaches are useful for application domains such as information extraction, question answering or summarisation, for which evidence from multiple sentences needs to be combined. We report on a …...

Semantic Textual Similarity (STS) seeks to measure the degree of semantic equivalence between two snippets of text. Similarity is ex- pressed on an ordinal scale that spans from semantic equivalence …...

Question answering forums are rapidly grow- ing in size with no effective automated abil- ity to refer to and reuse answers already avail- able for previous posted questions. In this …...

Temporal common sense has applications in AI tasks such as QA, multi-document summa- rization, and human-AI communication. We propose the task of sequencing – given a jum- bled set of …...

AbstractQuestions play a prominent role in social interactions, performing rhetorical functions that go beyond that of simple informational exchange. The surface form of a question can signal the intention and …...

AbstractWe present an approach for answering questions that span multiple sentences and exhibit sophisticated cross-sentence anaphoric phenomena, evaluating on a rich source of such questions – the math portion of …...

AbstractWe propose to use question answering (QA) data from Web forums to train chat-bots from scratch, i.e., without dialog data. First, we extract pairs of question and answer sentences from …...

AbstractWe present a framework for question answering that can efficiently scale to longer documents while maintaining or even improving performance of state-of-the-art models. While most successful approaches for reading comprehension …...

AbstractSemantic parsing shines at analyzing complex natural language that involves composition and computation over multiple pieces of evidence. However, datasets for semantic parsing contain many factoid questions that can be …...

AbstractRapid progress has been made towards question answering (QA) systems that can extract answers from text. Existing neural approaches make use of expensive bi-directional attention mechanisms or score all possible …...

AbstractQuestion answering (QA) systems are sensitive to the many different ways natural language expresses the same information need. In this paper we turn to paraphrases as a means of capturing …...

AbstractHumans as well as animals are good at imitation. Inspired by this, the learning by demonstration view of machine learning learns to perform a task from detailed example demonstrations. In …...

AbstractRecent development of large-scale question answering (QA) datasets triggered a substantial amount of research into end-to-end neural architectures for QA. Increasingly complex systems have been conceived without comparison to simpler …...

AbstractWe present a memory augmented neural network for natural language understanding: Neural Semantic Encoders. NSE is equipped with a novel memory update rule and has a variable sized encoding memory …...

AbstractBuilding knowledge bases (KB) automatically from text corpora is crucial for many applications such as question answering and web search. The problem is very challenging and has been divided into …...

AbstractRecent work in semantic parsing for question answering has focused on long and complicated questions, many of which would seem unnatural if asked in a normal conversation between two humans. …...

AbstractFor many applications of question answering (QA), being able to explain why a given model chose an answer is critical. However, the lack of labeled data for answer justifications makes …...

AbstractThere are few corpora that endeavor to represent the semantic content of entire documents. We present a corpus that accomplishes one way of capturing document level semantics, by annotating coreference …...

We constructed a large annotated dataset of zero pronouns that correspond to adjuncts marked by -de (translated to English as in, at, by or with) in Japanese. Adjunct zero anaphora …...

AbstractWe introduce a new type of deep contextualized word representation that models both (1) complex characteristics of word use (e.g., syntax and semantics), and (2) how these uses vary across …...

AbstractModeling hypernymy, such as poodle is-a dog, is an important generalization aid to many NLP tasks, such as entailment, relation extraction, and question answering. Supervised learning from labeled hypernym sources, …...

AbstractThe decision making processes of deep networks are difficult to understand and while their accuracy often improves with increased architectural complexity, so too does their opacity. Practical use of machine …...

AbstractQuestion answering over knowledge graphs is an important problem of interest both commercially and academically. There is substantial interest in the class of natural language questions that can be answered …...

AbstractQuestion answering (QA) and question generation (QG) are closely related tasks that could improve each other; however, the connection of these two tasks is not well explored in literature. In …...

AbstractThe first stage of every knowledge base question answering approach is to link entities in the input question. We investigate entity linking in the context of question answering task and …...

AbstractWe present bot#1337: a dialog system developed for the 1st NIPS Conversational Intelligence Challenge 2017 (ConvAI). The aim of the competition was to implement a bot capable of conversing with …...

AbstractOpen Information Extraction (OIE) is the task of the unsupervised creation of structured information from text. OIE is often used as a starting point for a number of downstream tasks …...

AbstractLarge scale knowledge graphs (KGs) such as Freebase are generally incomplete. Reasoning over multi-hop (mh) KG paths is thus an important capability that is needed for question answering or other …...

AbstractDeep learning models are often not easily adaptable to new tasks and require task-specific adjustments. The differentiable neural computer (DNC), a memory-augmented neural network, is designed as a general problem …...

AbstractBuilding curious machines that can answer as well as ask questions is an important challenge for AI. The two tasks of question answering and question generation are usually tackled separately …...

AbstractAlthough transfer learning has been shown to be successful for tasks like object and speech recognition, its applicability to question answering (QA) has yet to be well-studied. In this paper, …...

AbstractWe analyzed the outputs of multiple question answering (QA) models applied to the Stanford Question Answering Dataset (SQuAD) to identify the core challenges for QA systems on this data set. …...

Alignments between natural language and Knowledge Base (KB) triples are an essential prerequisite for training machine learning approaches employed in a variety of Natural Language Processing problems. These include Relation …...

AbstractIn this paper, we study the problem of question answering over knowledge base. We identify that the primary bottleneck in this problem is the difficulty in accurately predicting the relations …...

AbstractReading comprehension (RC)—in contrast to information retrieval—requires integrating information and reasoning about events, entities, and their relations across a full document. Question answering is conventionally used to assess RC ability, …...

Automatic text comprehension is an arduous task of automatic natural language processing. This task has been addressed in several NLP applications, namely, question-answering, information retrieval, etc. In some cases, these …...

AbstractExisting research on question answering (QA) and comprehension reading (RC) are mainly focused on the resource-rich language like English. In recent times, the rapid growth of multi-lingual web content has …...

Developing methods of automated inference that are able to provide users with compelling human-readable justifications for why the answer to a question is correct is critical for domains such as …...

In the insurance industry, lots of effort is putting into helping the customer to solve their problems that occurred during and after purchasing cycle and helping telemarketers to practice selling …...

AbstractQuestion answering (QA) using textual sources for purposes such as reading comprehension (RC) has attracted much attention. This study focuses on the task of explainable multi-hop QA, which requires the …...

AbstractSemantic role labeling (SRL) is an important task for understanding natural languages, where the objective is to analyse propositions expressed by the verb and to identify each word that bears …...

AbstractQuestion-answering plays an important role in e-commerce as it allows potential customers to actively seek crucial information about products or services to help their purchase decision making. Inspired by the …...

AbstractWe introduce a concept of a virtual discourse tree to improve question answering (Q/A) recall for complex, multi-sentence questions. Augmenting the discourse tree of an answer with tree fragments obtained …...

AbstractCalculating Semantic Textual Similarity (STS) plays a significant role in many applications such as question answering, document summarisation, information retrieval and information extraction. All modern state of the art STS …...

AbstractWe consider a novel question answering (QA) task where the machine needs to read from large streaming data (long documents or videos) without knowing when the questions will be given, …...

AbstractIn this paper, we develop a novel Sparse Self-Attention Fine-tuning model (referred as SSAF) which integrates sparsity into self-attention mechanism to enhance the fine-tuning performance of BERT. In particular, sparsity …...

AbstractScenario-based question answering (SQA) has attracted increasing research attention. It typically requires retrieving and integrating knowledge from multiple sources, and applying general knowledge to a specific case described by a …...

AbstractLexically-constrained sequence decoding allows for explicit positive or negative phrase-based constraints to be placed on target output strings in generation tasks such as machine translation or monolingual text rewriting. We …...

AbstractContextualized word representations are able to give different representations for the same word in different contexts, and they have been shown to be effective in downstream natural language processing tasks, …...

AbstractInterpretability of machine learning (ML) models becomes more relevant with their increasing adoption. In this work, we address the interpretability of ML based question answering (QA) models on a combination …...

AbstractThere has been an increased interest in multimodal language processing including multimodal dialog, question answering, sentiment analysis, and speech recognition. However, naturally occurring multimodal data is often imperfect as a …...

AbstractWe present the Natural Questions corpus, a question answering data set. Questions consist of real anonymized, aggregated queries issued to the Google search engine. An annotator is presented with a …...

AbstractStandard accuracy metrics indicate that modern reading comprehension systems have achieved strong performance in many question answering datasets. However, the extent these systems truly understand language remains unknown, and existing …...

AbstractPrivacy policies are long and complex documents that are difficult for users to read and understand. Yet, they have legal effects on how user data can be collected, managed and …...

AbstractTheory of mind, i.e., the ability to reason about intents and beliefs of agents is an important task in artificial intelligence and central to resolving ambiguous references in natural language …...

AbstractThe processing of medical information is not a trivial task for medical non-experts. The paper presents an artificial assistant designed to facilitate a reliable access to medical online contents. Interactions …...

AbstractWhile evaluating an answer choice for Reading Comprehension task, other answer choices available for the question and the answers of related questions about the same paragraph often provide valuable information. …...

AbstractAspect-based sentiment analysis (ABSA), which aims to identify fine-grained opinion polarity towards a specific aspect, is a challenging subtask of sentiment analysis (SA). In this paper, we construct an auxiliary …...

AbstractSemantic similarity is an area of Natural Language Processing that is useful for several downstream applications, such as machine translation, natural language generation, information retrieval, or question answering. The task …...

AbstractThe abundant semi-structured data on the Web, such as HTML-based tables and lists, provide commercial search engines a rich information source for question answering (QA). Different from plain text passages …...

AbstractThe increasing amount of semi-structured and unstructured data on tourism websites brings a need for information extraction (IE) so as to construct a Tourism-domain Knowledge Graph (TKG), which is helpful …...

AbstractAutomatic definition extraction from texts is an important task that has numerous applications in several natural language processing fields such as summarization, analysis of scientific texts, automatic taxonomy generation, ontology …...

AbstractThis paper proposes a novel miscellaneous-context-based method to convert a sentence into a knowledge embedding in the form of a directed graph. We adopt the idea of conceptual graphs to …...

AbstractThe SARS-CoV-2 (COVID-19) pandemic spotlighted the importance of moving quickly with biomedical research. However, as the number of biomedical research papers continue to increase, the task of finding relevant articles …...

AbstractThere has been an influx of biomedical domain-specific language models, showing language models pre-trained on biomedical text perform better on biomedical domain benchmarks than those trained on general domain text …...

AbstractWe present BlockBERT, a lightweight and efficient BERT model for better modeling long-distance dependencies. Our model extends BERT by introducing sparse block structures into the attention matrix to reduce both …...

AbstractWe address the problem of calibrating prediction confidence for output entities of interest in natural language processing (NLP) applications. It is important that NLP applications such as named entity recognition …...

AbstractThe traditional image captioning task uses generic reference captions to provide textual information about images. Different user populations, however, will care about different visual aspects of images. In this paper, …...

AbstractMost pre-trained language models (PLMs) construct word representations at subword level with Byte-Pair Encoding (BPE) or its variations, by which OOV (out-of-vocab) words are almost avoidable. However, those methods split …...

AbstractWe introduce CHIME, a cross-passage hierarchical memory network for question answering (QA) via text generation. It extends XLNet introducing an auxiliary memory module consisting of two components: the context memory …...

AbstractQuestion answering and conversational systems are often baffled and need help clarifying certain ambiguities. However, limitations of existing datasets hinder the development of large-scale models capable of generating and utilising …...

AbstractIn this paper, we present CorefQA, an accurate and extensible approach for the coreference resolution task. We formulate the problem as a span prediction task, like in question answering: A …...

AbstractIn this paper, we propose Cross-Thought, a novel approach to pre-training sequence encoder, which is instrumental in building reusable sequence embeddings for large-scale NLP tasks such as question answering. Instead …...

AbstractRelation extraction (RE) has been extensively studied due to its importance in real-world applications such as knowledge base construction and question answering. Most of the existing works train the models …...

AbstractEntity set expansion, aiming at expanding a small seed entity set with new entities belonging to the same semantic class, is a critical task that benefits many downstream NLP and …...

AbstractIntermediate-task training—fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task—often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate …...

AbstractDespite achieving prominent performance on many important tasks, it has been reported that neural networks are vulnerable to adversarial examples. Previously studies along this line mainly focused on semantic tasks …...

AbstractThe problem of event extraction requires detecting the event trigger and extracting its corresponding arguments. Existing work in event argument extraction typically relies heavily on entity recognition as a preprocessing/concurrent …...

AbstractIn this work, we aim at equipping pre-trained language models with structured knowledge. We present two self-supervised tasks learning over raw text with the guidance from knowledge graphs. Building upon …...

AbstractCode-switching is the use of more than one language in the same conversation or utterance. Recently, multilingual contextual embedding models, trained on multiple monolingual corpora, have shown promising results on …...

AbstractQuestion Answering (QA) has shown great success thanks to the availability of large-scale datasets and the effectiveness of neural models. Recent research works have attempted to extend these successes to …...

AbstractAttribution methods assess the contribution of inputs to the model prediction. One way to do so is erasure: a subset of inputs is considered irrelevant if it can be removed …...

AbstractIn order to facilitate natural language understanding, the key is to engage commonsense or background knowledge. However, how to engage commonsense effectively in question answering systems is still under exploration …...

AbstractExisting machine reading comprehension (MRC) models do not scale effectively to real-world applications like web-level information retrieval and question answering (QA). We argue that this stems from the nature of …...

AbstractNamed entity disambiguation is an important task that plays the role of bridge between text and knowledge. However, the performance of existing methods drops dramatically for short text, which is …...

AbstractOpen attribute value extraction for emerging entities is an important but challenging task. A lot of previous works formulate the problem as a question-answering (QA) task. While the collections of …...

AbstractState-of-the-art question answering (QA) relies upon large amounts of training data for which labeling is time consuming and thus expensive. For this reason, customizing QA systems is challenging. As a …...

AbstractIn this paper, we introduce LiveQA, a new question answering dataset constructed from play-by-play live broadcast. It contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games, …...

AbstractQuestion answering (QA) models have shown rapid progress enabled by the availability of large, high-quality benchmark datasets. Such annotated datasets are difficult and costly to collect, and rarely exist in …...

AbstractThe incompleteness of knowledge base (KB) is a vital factor limiting the performance of question answering (QA). This paper proposes a novel QA method by leveraging text information to enhance …...

AbstractAnswering questions in many real-world applications often requires complex and precise information excerpted from texts spanned across a long document. However, currently no such annotated dataset is publicly available, which …...

AbstractIn the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited and has not been …...

AbstractWe propose a newly annotated dataset for information extraction on recipes. Unlike previous approaches to machine comprehension of procedural texts, we avoid a priori pre-defining domain-specific predicates to recognize (e.g., …...

AbstractExisting work on augmenting question answering (QA) models with external knowledge (e.g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model’s prediction rationale. In …...

AbstractWe present ScholarlyRead, span-of-word-based scholarly articles’ Reading Comprehension (RC) dataset with approximately 10K manually checked passage-question-answer instances. ScholarlyRead was constructed in semi-automatic way. We consider the articles from two popular …...

AbstractThe aim of all Question Answering (QA) systems is to generalize to unseen questions. Current supervised methods are reliant on expensive data annotation. Moreover, such annotations can introduce unintended annotator …...

AbstractSubjectivity is the expression of internal opinions or beliefs which cannot be objectively observed or verified, and has been shown to be important for sentiment analysis and word-sense disambiguation. Furthermore, …...

AbstractAdversarial attacks against natural language processing systems, which perform seemingly innocuous modifications to inputs, can induce arbitrary mistakes to the target models. Though raised great concerns, such adversarial attacks can …...

AbstractQuestion Answering (QA) is in increasing demand as the amount of information available online and the desire for quick access to this content grows. A common approach to QA has …...

AbstractContextual embeddings derived from transformer-based neural language models have shown state-of-the-art performance for various tasks such as question answering, sentiment analysis, and textual similarity in recent years. Extensive work shows …...

AbstractHuman conversations are complicated and building a human-like dialogue agent is an extremely challenging task. With the rapid development of deep learning techniques, data-driven models become more and more prevalent …...

AbstractTime-Offset Interaction Applications (TOIAs) are systems that simulate face-to-face conversations between humans and digital human avatars recorded in the past. Developing a well-functioning TOIA involves several research areas: artificial intelligence, …...

AbstractQuestion and answer generation is a data augmentation method that aims to improve question answering (QA) models given the limited amount of human labeled data. However, a considerable gap remains …...

AbstractWe aim to improve question answering (QA) by decomposing hard questions into simpler sub-questions that existing QA systems are capable of answering. Since labeling questions with decompositions is cumbersome, we …...

AbstractWhile models have reached superhuman performance on popular question answering (QA) datasets such as SQuAD, they have yet to outperform humans on the task of question answering itself. In this …...

With the rapid advancement of machine learning and deep learning, a great breakthrough has been achieved in many areas of natural language processing in recent years. Complex language tasks, such …...

AbstractReaders of academic research papers often read with the goal of answering specific questions. Question Answering systems that can answer those questions can make consumption of the content much more …...

AbstractAtomic clauses are fundamental text units for understanding complex sentences. Identifying the atomic sentences within complex sentences is important for applications such as summarization, argument mining, discourse analysis, discourse parsing, …...

Recent advancements in transformer-based models have greatly improved the ability of Question Answering (QA) systems to provide correct answers; in particular, answer sentence selection (AS2) models, core components of retrieval-based …...

Multilingual language models have been a crucial breakthrough as they considerably re- duce the need of data for under-resourced languages. Nevertheless, the superiority of language-specific models has already been proven …...

AbstractQuestion answering (QA) over a knowledge graph (KG) is a task of answering a natural language (NL) query using the information stored in KG. In a real-world industrial setting, this …...

AbstractRecent years have witnessed various types of generative models for natural language generation (NLG), especially RNNs or transformer based sequence-to-sequence models, as well as variational autoencoder (VAE) and generative adversarial …...

AbstractQuestion generation has recently shown impressive results in customizing question answering (QA) systems to new domains. These approaches circumvent the need for manually annotated training data from the new domain …...

AbstractPolicy compliance detection is the task of ensuring that a scenario conforms to a policy (e.g. a claim is valid according to government rules or a post in an online …...

AbstractShort textual descriptions of entities provide summaries of their key attributes and have been shown to be useful sources of background knowledge for tasks such as entity linking and question …...

AbstractThis paper explores learning rich self-supervised entity representations from large amounts of associated text. Once pre-trained, these models become applicable to multiple entity-centric tasks such as ranked retrieval, knowledge base …...

Natural language inference (NLI) is formu- lated as a unified framework for solving vari- ous NLP problems such as relation extraction, question answering, summarization, etc. It has been studied intensively …...

AbstractFine-tuning pre-trained language models for downstream tasks has become a norm for NLP. Recently it is found that intermediate training can improve performance for fine-tuning language models for target tasks, …...

AbstractThe factual knowledge acquired during pre-training and stored in the parameters of Language Models (LMs) can be useful in downstream tasks (e.g., question answering or textual inference). However, some facts …...

AbstractRecently, end-to-end (E2E) trained models for question answering over knowledge graphs (KGQA) have delivered promising results using only a weakly supervised dataset. However, these models are trained and evaluated in …...

AbstractSummarisation of reviews aims at compressing opinions expressed in multiple review documents into a concise form while still covering the key opinions. Despite the advancement in summarisation models, evaluation metrics …...

AbstractQuestion answering (QA) primarily descends from two branches of research: (1) Alan Turing’s investigation of machine intelligence at Manchester University and (2) Cyril Cleverdon’s comparison of library card catalog indices …...

AbstractIn several question answering benchmarks, pretrained models have reached human parity through fine-tuning on an order of 100,000 annotated questions and answers. We explore the more realistic few-shot setting, where …...

AbstractThe task of learning from only a few examples (called a few-shot setting) is of key importance and relevance to a real-world setting. For question answering (QA), the current state-of-the-art …...

AbstractNatural language (NL) explanations of model predictions are gaining popularity as a means to understand and verify decisions made by large black-box pre-trained models, for tasks such as Question Answering …...

AbstractThe sheer volume of financial statements makes it difficult for humans to access and analyze a business’s financials. Robust numerical reasoning likewise faces unique challenges in this domain. In this …...

AbstractEvent forecasting is a challenging, yet important task, as humans seek to constantly plan for the future. Existing automated forecasting studies rely mostly on structured data, such as time-series or …...

AbstractThe input vocabulary and the representations learned are crucial to the performance of neural NLP models. Using the full vocabulary results in less explainable and more memory intensive models, with …...

AbstractDespite recent progress, state-of-the-art question answering models remain vulnerable to a variety of adversarial attacks. While dynamic adversarial data collection, in which a human annotator tries to write examples that …...

AbstractThis paper studies joint models for selecting correct answer sentences among the top k provided by answer sentence selection (AS2) modules, which are core components of retrieval-based Question Answering (QA) …...

We study the problem of injecting knowl- edge into large pre-trained models like BERT and RoBERTa. Existing methods typically update the original parameters of pre-trained models when injecting knowledge. However, …...

To train a question answering model based on machine reading comprehension (MRC), sig- nificant effort is required to prepare annotated training data composed of questions and their answers from contexts. …...

Model-agnostic meta-learning (MAML) has been recently put forth as a strategy to learn resource-poor languages in a sample-efficient fashion. Nevertheless, the properties of these languages are often not well represented …...

AbstractQuestion Answering (QA) tasks requiring information from multiple documents often rely on a retrieval model to identify relevant information for reasoning. The retrieval model is typically trained to maximize the …...

AbstractQuestion Answering (QA) has been successfully applied in scenarios of human-computer interaction such as chatbots and search engines. However, for the specific biomedical domain, QA systems are still immature due …...

AbstractMultilingual T5 pretrains a sequence-to-sequence model on massive monolingual texts, which has shown promising results on many cross-lingual tasks. In this paper, we improve multilingual text-to-text transfer Transformer with translation …...

Multi-party dialogue machine reading compre- hension (MRC) raises an even more challeng- ing understanding goal on dialogue with more than two involved speakers, compared with the traditional plain passage style …...

AbstractNarrative analysis is becoming increasingly important for a number of linguistic tasks including summarization, knowledge extraction, and question answering. We present a novel approach for narrative event representation using attention …...

Open Knowledge Graphs (OpenKG) refer to a set of (head noun phrase, relation phrase, tail noun phrase) triples such as (tesla, return to, new york) extracted from a corpus using …...

How much information do NLP tasks really need from a transformer’s attention mecha- nism at application-time (inference)? From recent work, we know that there is sparsity in transformers and that …...

AbstractFor many tasks, state-of-the-art results have been achieved with Transformer-based architectures, resulting in a paradigmatic shift in practices from the use of task-specific architectures to the fine-tuning of pre-trained language …...

AbstractWe introduce a new dataset for Question Rewriting in Conversational Context (QReCC), which contains 14K conversations with 80K question-answer pairs. The task in QReCC is to find answers to conversational …...

AbstractOur goal is to deliver a new task and leaderboard to stimulate research on question answering and pre-trained language models (PTLMs) to understand a significant instructional document, e.g., an introductory …...

AbstractDense retrieval methods have shown great promise over sparse retrieval methods in a range of NLP problems. Among them, dense phrase retrieval—the most fine-grained retrieval unit—is appealing because phrases can …...

AbstractNeural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability. Inspired by recent work …...

AbstractKnowledge-intensive tasks such as question answering often require assimilating information from different sections of large inputs such as books or article collections. We propose ReadTwice, a simple and effective technique …...

AbstractTables in web documents are pervasive and can be directly used to answer many of the queries searched on the web, motivating their integration in question answering. Very often information …...

AbstractRecently, the performance of Pre-trained Language Models (PLMs) has been significantly improved by injecting knowledge facts to enhance their abilities of language understanding. For medical domains, the background knowledge sources …...

AbstractIn recent years, we have seen a colossal effort in pre-training multilingual text encoders using large-scale corpora in many languages to facilitate cross-lingual transfer learning. However, due to typological differences …...

AbstractCoupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on the Question Answering task. However, most of those datasets are in English, and the …...

AbstractHybrid data combining both tabular and textual content (e.g., financial reports) are quite pervasive in the real world. However, Question Answering (QA) over such hybrid data is largely neglected in …...

AbstractWe propose a general framework called Text Modular Networks(TMNs) for building interpretable systems that learn to solve complex tasks by decomposing them into simpler ones solvable by existing models. To …...

AbstractTracking entities throughout a procedure described in a text is challenging due to the dynamic nature of the world described in the process. Firstly, we propose to formulate this task …...

AbstractA desirable property of a reference-based evaluation metric that measures the content quality of a summary is that it should estimate how much information that summary has in common with …...

AbstractIdentifying whether a word carries the same meaning or different meaning in two contexts is an important research area in natural language processing which plays a significant role in many …...

AbstractThe increment of toxic comments on online space is causing tremendous effects on other vulnerable users. For this reason, considerable efforts are made to deal with this, and SemEval-2021 Task …...

AbstractMost question answering tasks focuses on predicting concrete answers, e.g., named entities. These tasks can be normally achieved by understanding the contexts without additional information required. In Reading Comprehension of …...

Recent advances in natural language process- ing (NLP) have the ability to transform how classroom learning takes place. Combined with the increasing integration of technology in today’s classrooms, NLP systems …...

AbstractMany Question-Answering (QA) datasets contain unanswerable questions, but their treatment in QA systems remains primitive. Our analysis of the Natural Questions (Kwiatkowski et al. 2019) dataset reveals that a substantial …...

AbstractIn this paper we propose a novel approach towards improving the efficiency of Question Answering (QA) systems by filtering out questions that will not be answered by them. This is …...

AbstractMultihop reasoning remains an elusive goal as existing multihop benchmarks are known to be largely solvable via shortcuts. Can we create a question answering (QA) dataset that, by construction, requires …...

AbstractEntities, as important carriers of real-world knowledge, play a key role in many NLP tasks. We focus on incorporating entity knowledge into an encoder-decoder framework for informative text generation. Existing …...

AbstractMultilingual pretrained models, while effective on monolingual data, need additional training to work well with code-switched text. In this work, we present a novel idea of training multilingual models with …...

AbstractAccess to external knowledge is essential for many natural language processing tasks, such as question answering and dialogue. Existing methods often rely on a parametric model that stores knowledge in …...

AbstractQuestion answering on knowledge bases (KBQA) poses a unique challenge for semantic parsing research due to two intertwined challenges: large search space and ambiguities in schema linking. Conventional ranking-based KBQA …...

AbstractIt is well documented that NLP models learn social biases, but little work has been done on how these biases manifest in model outputs for applied tasks like question answering …...

AbstractQuestion-Answer (QA) is one of the effective methods for storing knowledge which can be used for future retrieval. As such, identifying mentions of questions and their answers in text is …...

AbstractQuestion Answering (QA) systems aim to return correct and concise answers in response to user questions. QA research generally assumes all questions are intelligible and unambiguous, which is unrealistic in …...

AbstractThere have been many efforts to try to understand what grammatical knowledge (e.g., ability to understand the part of speech of a token) is encoded in large pre-trained language models …...

AbstractModels of natural language understanding often rely on question answering and logical inference benchmark challenges to evaluate the performance of a system. While informative, such task-oriented evaluations do not assess …...

AbstractWith their Discovery of Inference Rules from Text (DIRT) algorithm, Lin and Pantel (2001) made a seminal contribution to the field of rule acquisition from text, by adapting the distributional …...

AbstractQuestion answering (QA) has demonstrated impressive progress in answering questions from customized domains. Nevertheless, domain adaptation remains one of the most elusive challenges for QA systems, especially when QA systems …...

AbstractCross-lingual transfer learning without labeled target language data or parallel text has been surprisingly effective in zero-shot cross-lingual classification, question answering, unsupervised machine translation, etc. However, some recent publications have …...

AbstractWhen people answer questions about a specific situation, e.g., “I cheated on my mid-term exam last week. Was that wrong?”, cognitive science suggests that they form a mental picture of …...

AbstractOur goal is a question-answering (QA) system that can show how its answers are implied by its own internal beliefs via a systematic chain of reasoning. Such a capability would …...

AbstractWhile many methods purport to explain predictions by highlighting salient features, what aims these explanations serve and how they ought to be evaluated often go unstated. In this work, we …...

AbstractQuestion answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity of high-quality QA datasets …...

AbstractQuantitative reasoning is an important aspect of question answering, especially when numeric and verbal cues interact to indicate sophisticated, multi-step programs. In this paper, we demonstrate how modeling the compositional …...

AbstractWe present IBR, an Iterative Backward Reasoning model to solve the proof generation tasks on rule-based Question Answering (QA), where models are required to reason over a series of textual …...

AbstractExisting question answering (QA) techniques are created mainly to answer questions asked by humans. But in educational applications, teachers often need to decide what questions they should ask, in order …...

AbstractLearning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering. In recent years, many research efforts …...

AbstractRecent studies show that Question Answering (QA) based on Answer Sentence Selection (AS2) can be improved by generating an improved answer from the top-k ranked answer sentences (termed GenQA). This …...

AbstractExplanations promise to bridge the gap between humans and AI, yet it remains difficult to achieve consistent improvement in AI-augmented human decision making. The usefulness of AI explanations depends on …...

AbstractMultilingual pre-trained models are known to suffer from the curse of multilinguality, which causes per-language performance to drop as they cover more languages. We address this issue by introducing language-specific …...

AbstractMathematical reasoning skills are essential for general-purpose intelligentsystems to perform tasks from grocery shopping to climate modeling. Towards evaluating and improving AI systems in this domain, we proposeLILA, a unified …...

AbstractQuestion answering over knowledge bases (KBQA) for complex questions is a challenging task in natural language processing. Recently, generation-based methods that translate natural language questions to executable logical forms have …...

AbstractWe introduce MetaICL (Meta-training for In-Context Learning), a new meta-training framework for few-shot learning where a pretrained language model is tuned to do in-context learning on a large set of …...

AbstractWe introduce Mintaka, a complex, natural, and multilingual dataset designed for experimenting with end-to-end question-answering models. Mintaka is composed of 20,000 question-answer pairs collected in English, annotated with Wikidata entities, …...

AbstractNumerical reasoning over hybrid data containing both textual and tabular content (e.g., financial reports) has recently attracted much attention in the NLP community. However, existing question answering (QA) benchmarks over …...

AbstractThis paper aims for a potential architectural improvement for multilingual learning and asks: Can different tasks from different languages be modeled in a monolithic framework, i.e. without any task/language-specific module? …...

AbstractWith the ever growing scale of neural models, knowledge distillation (KD) attracts more attention as a prominent tool for neural model compression. However, there are counter intuitive observations in the …...

AbstractQuestion Answering (QA) is a longstanding challenge in natural language processing. Existing QA works mostly focus on specific question types, knowledge domains, or reasoning skills. The specialty in QA research …...

AbstractWe propose a pre-training objective based on question answering (QA) for learning general-purpose contextual representations, motivated by the intuition that the representation of a phrase in a passage should encode …...

AbstractThis paper introduces the question answering paradigm as a way to explore digitized archive collections for Social Science studies. In particular, we are interested in evaluating largely studied question generation …...

AbstractDeep NLP models have been shown to be brittle to input perturbations. Recent work has shown that data augmentation using counterfactuals — i.e. minimally perturbed inputs — can help ameliorate …...

AbstractQuestion answering (QA) is one of the most common NLP tasks that relates to named entity recognition, fact extraction, semantic search and some other fields. In industry, it is much …...

AbstractNLP benchmarks have largely focused on short texts, such as sentences and paragraphs, even though long texts comprise a considerable amount of natural language in the wild. We introduce SCROLLS, …...

AbstractNatural language modeling with limited training data is a challenging problem, and many algorithms make use of large-scale pretrained language models (PLMs) for this due to its great generalization ability. …...

AbstractDeep learning models have significantly advanced the state of the art of question answering systems. However, the majority of datasets available for training such models have been annotated by humans, …...

AbstractSynthesizing QA pairs with a question generator (QG) on the target domain has become a popular approach for domain adaptation of question answering (QA) models. Since synthetic questions are often …...

AbstractWe present Twin Answer Sentences Attack (TASA), an adversarial attack method for question answering (QA) models that produces fluent and grammatical adversarial contexts while maintaining gold answers. Despite phenomenal progress …...

AbstractThe predictions of question answering (QA) systems are typically evaluated against manually annotated finite sets of one or more answers. This leads to a coverage limitation that results in underestimating …...

AbstractOur goal is a teachable reasoning system for question-answering (QA), where a user can interact with faithful answer explanations, and correct its errors so that the system improves over time. …...

AbstractGrounded text generation systems often generate text that contains factual inconsistencies, hindering their real-world applicability. Automatic factual consistency evaluation may help alleviate this limitation by accelerating evaluation cycles, filtering inconsistent …...

AbstractParsing natural language questions into executable logical forms is a useful and interpretable way to perform question answering on structured data such as knowledge bases (KB) or databases (DB). However, …...

AbstractThere is a growing interest in dataset generation recently due to the superior generative capacity of large pre-trained language models (PLMs). In this paper, we study a flexible and efficient …...

AbstractWe present bgGLUE (Bulgarian General Language Understanding Evaluation), a benchmark for evaluating language models on Natural Language Understanding (NLU) tasks in Bulgarian. Our benchmark includes NLU tasks targeting a variety …...

AbstractCausal reasoning is a critical component of human cognition and is required across a range of question-answering (QA) tasks (such as abductive reasoning, commonsense QA, and procedural reasoning). Research on …...

AbstractDo question answering (QA) modeling improvements (e.g., choice of architecture and training procedure) hold consistently across the diverse landscape of QA benchmarks? To study this question, we introduce the notion …...

AbstractFigurative language is commonplace in natural language, and while making communication memorable and creative, can be difficult to understand. In this work, we investigate the robustness of Question Answering (QA) …...

AbstractWe present ELQA, a corpus of questions and answers in and about the English language. Collected from two online forums, the >70k questions (from English learners and others) cover wide-ranging …...

Automatic generation of questions about the given context is useful for the adaptation of question-answering systems or to support edu- cation. We trained and evaluated a model that generates a …...

AbstractIn this work, we propose a method for incorporating question-answering (QA) signals into a summarization model. Our method identifies salient noun phrases (NPs) in the input document by automatically generating …...

In this research, we investigated GPT-4 as a question-answering model for the Holy Quran. As a first step, we built the Quran ques- tion–answer pair (QUQA) dataset, comprising 2,189 questions, …...

AbstractIn this paper, we describe our approach to the clickbait spoiling task of SemEval 2023.The core idea behind our system is to leverage pre-trained models capable of Question Answering (QA) …...

AbstractPrompt tuning (PT) which only tunes the embeddings of an additional sequence of tokens per task, keeping the pre-trained language model (PLM) frozen, has shown remarkable performance in few-shot learning. …...

AbstractIn tasks like semantic parsing, instruction following, and question answering, standard deep networks fail to generalize compositionally from small datasets. Many existing approaches overcome this limitation with model architectures that …...

AbstractWith the ubiquitous use of online meeting platforms and robust automatic speech recognition systems, meeting transcripts have emerged as a promising domain for natural language tasks. Most recent works on …...

AbstractThe recent explosion of question-answering (QA) datasets and models has increased the interest in the generalization of models across multiple domains and formats by either training on multiple datasets or …...

AbstractLarge-scale pre-trained models (PTMs) have been widely used in document-oriented NLP tasks, such as question answering. However, the encoding-task coupling requirement results in the repeated encoding of the same documents …...

AbstractLarge language models (LLMs) have been shown to perform well in answering questions and in producing long-form texts, both in few-shot closed-book settings. While the former can be validated using …...

AbstractLanguage models (LMs) now excel at many tasks such as question answering, reasoning, and dialog. However, they sometimes generate unsupported or misleading content. A user cannot easily determine whether their …...

AbstractPrior studies in privacy policies frame the question answering (QA) task as identifying the most relevant text segment or a list of sentences from a policy document given a user …...

AbstractThis paper describes an approach to automat- ically close the knowledge gap of Clickbait- Posts via a transformer model trained for Question-Answering, augmented by a task- specific post-processing step. This …...

AbstractMasked language modeling (MLM) plays a key role in pretraining large language models. But the MLM objective is often dominated by high-frequency words that are sub-optimal for learning factual knowledge. …...

AbstractZero-Shot Relation Extraction (ZRE) is the task of Relation Extraction where the training and test sets have no shared relation types. This very challenging domain is a good test of …...

AbstractPre-trained language models (PLMs) have shown unprecedented potential in various fields, especially as the backbones for question-answering (QA) systems. However, they tend to be easily deceived by tricky questions such …...