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Many machine learning methods have recently been applied to natural lan- guage processing tasks. Among them, the Winnow algorithm has been ar- gued to be particularly suitable for NLP problems, …...

This paper presents a Chinese word segmen- tation system which can adapt to different domains and standards. We first present a sta- tistical framework where domain-specific words are identified in …...

Word Sense Disambiguation suffers from a long-standing problem of knowledge ac- quisition bottleneck. Although state of the art supervised systems report good accu- racies for selected words, they have not …...

. This paper is concerned with Chinese word segmentation, which is regarded as a character based tagging problem under conditional random field framework. It is different in our method that …...

Recognizing textual entailment is a chal- lenging problem and a fundamental com- ponent of many applications in natural language processing. We present a novel framework for recognizing textual entail- ment …...

Letter-to-phoneme conversion generally requires aligned training data of letters and phonemes. Typically, the align- ments are limited to one-to-one align- ments. We present a novel technique of training with many-to-many …...

This paper summarizes FBK-irst participa- tion at the lexical substitution task of the SEMEVAL competition. We submitted two different systems, both exploiting synonym lists extracted from dictionaries. For each word …...

We describe a parsing approach that makes use of the perceptron algorithm, in conjunction with dynamic programming methods, to recover full constituent-based parse trees. The formalism allows a rich set …...

In this paper we develop an approach to tackle the problem of verb selection for learners of English as a second language (ESL) by using features from the output of …...

The problem of authorship attribution – at- tributing texts to their original authors – has been an active research area since the end of the 19th century, attracting increased interest …...

Twitter provides access to large volumes of data in real time, but is notoriously noisy, hampering its utility for NLP. In this paper, we target out-of-vocabulary words in short text …...

Context Peter D. Turney Inst. for Info. Tech. NRC Canada Ottawa, Canada peter.turney@nrc-cnrc.gc.ca Yair Neuman Dept. of Education Ben-Gurion Univ. Beer-Sheva, Israel yneuman@bgu.ac.il Dan Assaf Dept. of Education Ben-Gurion Univ. …...

We present a simple method for transferring dependency parsers from source languages with labeled training data to target languages without labeled training data. We first demon- strate that delexicalized parsers …...

AbstractWe introduce a novel nonparametric Bayesian model for the induction of Combinatory Categorial Grammars from POS-tagged text. It achieves state of the art performance on a number of languages, and …...

We present a novel unsupervised approach to detecting the compositionality of multi-word expressions. We compute the compositional- ity of a phrase through substituting the con- stituent words with their “neighbours” …...

Measuring term informativeness is a funda- mental NLP task. Existing methods, mostly based on statistical information in corpora, do not actually measure informativeness of a term with regard to its …...

This paper investigates the use of neural networks for the acquisition of selectional preferences. Inspired by recent advances of neural network models for NLP applica- tions, we propose a neural …...

When Part-of-Speech annotated data is scarce, e.g. for under-resourced lan- guages, one can turn to cross-lingual trans- fer and crawled dictionaries to collect par- tially supervised data. We cast this …...

Community question answering (CQA) has become an important service due to the popularity of CQA archives on the web. A distinctive feature is that CQA services usually organize questions into …...

We show that it is possible to reliably dis- criminate whether a syntactic construction is meant literally or metaphorically using lexical semantic features of the words that participate in the …...

We present a novel approach for induc- ing unsupervised dependency parsers for languages that have no labeled training data, but have translated text in a resource- rich language. We train …...

This paper presents a novel technique for empty category (EC) detection using dis- tributed word representations. A joint model is learned from the labeled data to map both the distributed …...

A key challenge for timeline summarization is to generate a concise, yet complete storyline from large collections of news stories. Pre- vious studies in extractive timeline generation are limited in …...

AbstractWe present a method for unsupervised open-domain relation discovery. In contrast to previous (mostly generative and agglomerative clustering) approaches, our model relies on rich contextual features and makes minimal independence …...

We study the problem of jointly aligning sen- tence constituents and predicting their sim- ilarities. While extensive sentence similar- ity data exists, manually generating reference alignments and labeling the similarities …...

We introduce a simple semi-supervised ap- proach to improve implicit discourse relation identification. This approach harnesses large amounts of automatically extracted discourse connectives along with their arguments to con- struct …...

AbstractReading comprehension (RC), aiming to understand natural texts and answer questions therein, is a challenging task. In this paper, we study the RC problem on the Stanford Question Answering Dataset …...

AbstractThere have been many attempts at automatically recognising author personality traits from text, typically incorporating linguistic features with conventional machine learning models, e.g. linear regression or Support Vector Machines. In …...

AbstractUnsupervised dependency parsing aims to learn a dependency parser from unannotated sentences. Existing work focuses on either learning generative models using the expectation-maximization algorithm and its variants, or learning discriminative …...

AbstractExisting approaches to automatic VerbNet-style verb classification are heavily dependent on feature engineering and therefore limited to languages with mature NLP pipelines. In this work, we propose a novel cross-lingual …...

AbstractThis paper proposes three distortion models to explicitly incorporate the word reordering knowledge into attention-based Neural Machine Translation (NMT) for further improving translation performance. Our proposed models enable attention mechanism …...

AbstractRecent advances in RST discourse parsing have focused on two modeling paradigms: (a) high order parsers which jointly predict the tree structure of the discourse and the relations it encodes; …...

AbstractGender identification in social networks is one of the most popular aspects of user profile learning. Traditionally it has been linked to author profiling, a difficult problem to solve because …...

AbstractBilingual Lexicon Induction is the task of learning word translations without bilingual parallel corpora. We model this task as a matrix completion problem, and present an effective and extendable framework …...

AbstractThis paper is concerned with building deep grammatical relation (GR) analysis using data-driven approach. To deal with this problem, we propose graph merging, a new perspective, for building flexible dependency …...

AbstractPrevious work on dialog act (DA) classification has investigated different methods, such as hidden Markov models, maximum entropy, conditional random fields, graphical models, and support vector machines. A few recent …...

AbstractEvents in text documents are interrelated in complex ways. In this paper, we study two types of relation: Event Coreference and Event Sequencing. We show that the popular tree-like decoding …...

AbstractGenerating natural language requires conveying content in an appropriate style. We explore two related tasks on generating text of varying formality: monolingual formality transfer and formality-sensitive machine translation. We propose …...

We propose the first multi-task learning model for joint Vietnamese word segmentation, part- of-speech (POS) tagging and dependency parsing. In particular, our model extends the BIST graph-based dependency parser (Kiper- …...

Semantic parsing targets at mapping human utterances into structured meaning represen- tations, such as logical forms, programming snippets, SQL queries etc. In this work, we focus on logical form generation, …...

AbstractAttention-based neural models were employed to detect the different aspects and sentiment polarities of the same target in targeted aspect-based sentiment analysis (TABSA). However, existing methods do not specifically pre-train …...

AbstractWe introduce a novel scheme for parsing a piece of text into its Abstract Meaning Representation (AMR): Graph Spanning based Parsing (GSP). One novel characteristic of GSP is that it …...

AbstractUnderstanding narrated instructional videos is important for both research and real-world web applications. Motivated by video dense captioning, we propose a model to generate procedure captions from narrated instructional videos …...

AbstractEnd-to-end training with Deep Neural Networks (DNN) is a currently popular method for metaphor identification. However, standard sequence tagging models do not explicitly take advantage of linguistic theories of metaphor …...

AbstractMany common character-level, string-to-string transduction tasks, e.g., grapheme-to-phoneme conversion and morphological inflection, consist almost exclusively of monotonic transduction. Neural sequence-to-sequence models with soft attention, non-monotonic models, outperform popular monotonic models. …...

AbstractGenerating from Abstract Meaning Representation (AMR) is an underspecified problem, as many syntactic decisions are not specified by the semantic graph. To explicitly account for this variation, we break down …...

AbstractVisual question answering (VQA) and image captioning require a shared body of general knowledge connecting language and vision. We present a novel approach to better VQA performance that exploits this …...

AbstractConcept normalization, the task of linking textual mentions of concepts to concepts in an ontology, is challenging because ontologies are large. In most cases, annotated datasets cover only a small …...

AbstractIn real-world scenarios, users usually have multiple intents in the same utterance. Unfortunately, most spoken language understanding (SLU) models either mainly focused on the single intent scenario, or simply incorporated …...

AbstractEmotion recognition in conversations (ERC) has received much attention recently in the natural language processing community. Considering that the emotions of the utterances in conversations are interactive, previous works usually …...

AbstractChinese spelling check is a challenging task due to the characteristics of the Chinese language, such as the large character set, no word boundary, and short word length. On the …...

AbstractMore recently, Named Entity Recognition hasachieved great advances aided by pre-trainingapproaches such as BERT. However, currentpre-training techniques focus on building lan-guage modeling objectives to learn a gen-eral representation, ignoring the …...

AbstractGeneral-purpose pretrained sentence encoders such as BERT are not ideal for real-world conversational AI applications; they are computationally heavy, slow, and expensive to train. We propose ConveRT (Conversational Representations from …...

AbstractUnderstanding discourse structures of news articles is vital to effectively contextualize the occurrence of a news event. To enable computational modeling of news structures, we apply an existing theory of …...

AbstractDocument-level relation extraction (RE) poses new challenges over its sentence-level counterpart since it requires an adequate comprehension of the whole document and the multi-hop reasoning ability across multiple sentences to …...

AbstractDiscourse representation tree structure (DRTS) parsing is a novel semantic parsing task which has been concerned most recently. State-of-the-art performance can be achieved by a neural sequence-to-sequence model, treating the …...

AbstractMulti-Domain Neural Machine Translation (NMT) aims at building a single system that performs well on a range of target domains. However, along with the extreme diversity of cross-domain wording and …...

AbstractImage captioning is a multimodal problem that has drawn extensive attention in both the natural language processing and computer vision community. In this paper, we present a novel image captioning …...

AbstractIn this paper, we study Multimodal Named Entity Recognition (MNER) for social media posts. Existing approaches for MNER mainly suffer from two drawbacks: (1) despite generating word-aware visual representations, their …...

AbstractMost existing work on event extraction (EE) either follows a pipelined manner or uses a joint structure but is pipelined in essence. As a result, these efforts fail to utilize …...

AbstractWe present a semi-supervised model which learns the semantics of negation purely through analysis of syntactic structure. Linguistic theory posits that the semantics of negation can be understood purely syntactically, …...

AbstractEfficient structure encoding for graphs with labeled edges is an important yet challenging point in many graph-based models. This work focuses on AMR-to-text generation – A graph-to-sequence task aiming to …...

AbstractSarcasm is a pervasive phenomenon in today’s social media platforms such as Twitter and Reddit. These platforms allow users to create multi-modal messages, including texts, images, and videos. Existing multi-modal …...

AbstractMany tasks are considered to be ‘solved’ in the computational linguistics literature, but the corresponding algorithms operate in ways which are radically different from human cognition. I illustrate this by …...

AbstractEvent information is usually scattered across multiple sentences within a document. The local sentence-level event extractors often yield many noisy event role filler extractions in the absence of a broader …...

AbstractThis paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information about the input passage. In order …...

AbstractMost previous event extraction studies assume a set of target event types and corresponding event annotations are given, which could be very expensive. In this paper, we work on a …...

AbstractRecent systems for converting natural language descriptions into regular expressions (regexes) have achieved some success, but typically deal with short, formulaic text and can only produce simple regexes. Real-world regexes …...

AbstractPersonality image captioning (PIC) aims to describe an image with a natural language caption given a personality trait. In this work, we introduce a novel formulation for PIC based on …...

AbstractExisting leading code comment generation approaches with the structure-to-sequence framework ignores the type information of the interpretation of the code, e.g., operator, string, etc. However, introducing the type information into …...

AbstractUnsupervised extractive document summarization aims to select important sentences from a document without using labeled summaries during training. Existing methods are mostly graph-based with sentences as nodes and edge weights …...

AbstractThe tasks of Rich Semantic Parsing, such as Abstract Meaning Representation (AMR), share similar goals with Information Extraction (IE) to convert natural language texts into structured semantic representations. To take …...

AbstractStance detection on social media can help to identify and understand slanted news or commentary in everyday life. In this work, we propose a new model for zero-shot stance detection …...

Structural syntax knowledge has been proven effective for semantic role labeling (SRL), while existing works mostly use only one sin- gleton syntax, such as either syntactic depen- dency or constituency …...

AbstractPre-trained language models have been shown to improve performance in many natural language tasks substantially. Although the early focus of such models was single language pre-training, recent advances have resulted …...

AbstractAspect-level sentiment analysis (ASA) has received much attention in recent years. Most existing approaches tried to leverage syntactic information, such as the dependency parsing results of the input text, to …...

AbstractNatural language processing for program synthesis has been widely researched. In this work, we focus on generating Bash commands from natural language invocations with explanations. We propose a novel transformer …...

AbstractIn this work, we focus on a more challenging few-shot intent detection scenario where many intents are fine-grained and semantically similar. We present a simple yet effective few-shot intent detection …...

AbstractWe present models which complete missing text given transliterations of ancient Mesopotamian documents, originally written on cuneiform clay tablets (2500 BCE - 100 CE). Due to the tablets’ deterioration, scholars …...

AbstractCurrent event-centric knowledge graphs highly rely on explicit connectives to mine relations between events. Unfortunately, due to the sparsity of connectives, these methods severely undermine the coverage of EventKGs. The …...

AbstractAspect term extraction aims to extract aspect terms from a review sentence that users have expressed opinions on. One of the remaining challenges for aspect term extraction resides in the …...

AbstractSocial networks face a major challenge in the form of rumors and fake news, due to their intrinsic nature of connecting users to millions of others, and of giving any …...

AbstractThe task of knowledge base population (KBP) aims to discover facts about entities from texts and expand a knowledge base with these facts. Previous studies shape end-to-end KBP as a …...

AbstractJoint entity and relation extraction is challenging due to the complex interaction of interaction between named entity recognition and relation extraction. Although most existing works tend to jointly train these …...

AbstractImplicit Event Argument Extraction seeks to identify arguments that play direct or implicit roles in a given event. However, most prior works focus on capturing direct relations between arguments and …...

AbstractWe present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot video and text understanding, without using any labels on downstream tasks. VideoCLIP trains a transformer for video …...

AbstractAdversarial attacks are a major challenge faced by current machine learning research. These purposely crafted inputs fool even the most advanced models, precluding their deployment in safety-critical applications. Extensive research …...

AbstractArgument mining (AM) is a challenging task as it requires recognizing the complex argumentation structures involving multiple subtasks. To handle all subtasks of AM in an end-to-end fashion, previous works …...

AbstractEvent argument extraction (EAE) aims to extract arguments with given roles from texts, which have been widely studied in natural language processing. Most previous works have achieved good performance in …...

AbstractAspect sentiment triplet extraction (ASTE) is a challenging subtask in aspect-based sentiment analysis. It aims to explore the triplets of aspects, opinions and sentiments with complex correspondence from the context. …...

AbstractQuotation extraction aims to extract quotations from written text. There are three components in a quotation: source refers to the holder of the quotation, cue is the trigger word(s), and …...

AbstractImplicit discourse relation recognition is a challenging task that involves identifying the sense or senses that hold between two adjacent spans of text, in the absense of an explicit connective …...

AbstractAutomatic email to-do item generation is the task of generating to-do items from a given email to help people overview emails and schedule daily work. Different from prior research on …...

AbstractFew-shot relation extraction (FSRE) has been a challenging problem since it only has a handful of training instances. Existing models follow a ‘one-for-all’ scheme where one general large model performs …...

AbstractThe Winograd Schema Challenge (WSC) consists of a set of anaphora resolution problems resolvable only by reasoning about world knowledge. This article describes the update of the existing French data …...

AbstractIn this paper, we propose LaPraDoR, a pretrained dual-tower dense retriever that does not require any supervised data for training. Specifically, we first present Iterative Contrastive Learning (ICoL) that iteratively …...

AbstractLogical reasoning of text requires identifying critical logical structures in the text and performing inference over them. Existing methods for logical reasoning mainly focus on contextual semantics of text while …...

AbstractWe target the complementary binary tasks of identifying whether a tweet is misogynous and, if that is the case, whether it is also aggressive. We compare two ways to address …...

AbstractWe study the challenge of learning causal reasoning over procedural text to answer “What if...” questions when external commonsense knowledge is required. We propose a novel multi-hop graph reasoning model …...

AbstractMost existing pre-trained language representation models (PLMs) are sub-optimal in sentiment analysis tasks, as they capture the sentiment information from word-level while under-considering sentence-level information. In this paper, we propose …...

Abstract“With the development of deep learning in recent years, text classification research has achieved remarkable results. However, text classification task often requires a large amount of annotated data, and data …...

AbstractCausal Emotion Entailment (CEE) aims to discover the potential causes behind an emotion in a conversational utterance. Previous works formalize CEE as independent utterance pair classification problems, with emotion and …...

AbstractIn a hate speech detection model, we should consider two critical aspects in addition to detection performance–bias and explainability. Hate speech cannot be identified based solely on the presence of …...

AbstractZero-shot cross-lingual named entity recognition (NER) aims at transferring knowledge from annotated and rich-resource data in source languages to unlabeled and lean-resource data in target languages. Existing mainstream methods based …...

AbstractThe ability to recognize emotions in conversations is necessary and important for the online chatbot to do tasks such as empathetic response generation and emotional support. Present researches mainly focus …...

AbstractImage-guided story ending generation (IgSEG) is to generate a story ending based on given story plots and ending image. Existing methods focus on cross-modal feature fusion but overlook reasoning and …...