StateOfTheArtMethods

In this paper we define a novel similarity measure between examples of textual en- tailments and we use it as a kernel func- tion in Support Vector Machines (SVMs). This …...

In opinion mining of product reviews, one of- ten wants to produce a summary of opinions based on product features/attributes. Howev- er, for the same feature, people can express it …...

Searching documents that are similar to a query document is an important component in modern information retrieval. Some ex- isting hashing methods can be used for effi- cient document similarity …...

AbstractRecognizing metaphors and identifying the source-target mappings is an important task as metaphorical text poses a big challenge for machine reading. To address this problem, we automatically acquire a metaphor …...

We propose a simple unsupervised ap- proach to detecting non-compositional components in multiword expressions based on Wiktionary. The approach makes use of the definitions, synonyms and trans- lations in Wiktionary, …...

Extracting opinion targets and opinion words from online reviews are two fun- damental tasks in opinion mining. This paper proposes a novel approach to col- lectively extract them with graph …...

We present a hybrid approach to sentence simplification which combines deep se- mantics and monolingual machine transla- tion to derive simple sentences from com- plex ones. The approach differs from …...

When it is not possible to compare the suspi- cious document to the source document(s) plagiarism has been committed from, the evi- dence of plagiarism has to be looked for …...

Although the distributional hypothesis has been applied successfully in many natural language processing tasks, systems using distributional information have been lim- ited to a single domain because the dis- tribution …...

Arabic, Hebrew, and similar languages are typi- cally written without diacritics, leading to ambigu- ity and posing a major challenge for core language processing tasks like speech recognition. Previous approaches …...

Traditional approaches to Chinese Seman- tic Role Labeling (SRL) almost heavily re- ly on feature engineering. Even worse, the long-range dependencies in a sentence can hardly be modeled by these …...

AbstractHypernym-hyponym (“is-a”) relations are key components in taxonomies, object hierarchies and knowledge graphs. While there is abundant research on is-a relation extraction in English, it still remains a challenge to …...

This paper presents a clustering approach that simultaneously identifies product features and groups them into aspect categories from on- line reviews. Unlike prior approaches that first extract features and then …...

Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon. The interpretation of events and entities is highly contextually dependent. …...

Taxonomic relation identification aims to rec- ognize the ‘is-a’ relation between two terms. Previous works on identifying taxonomic re- lations are mostly based on statistical and lin- guistic approaches, but …...

Document-level sentiment classification aims to predict user’s overall sentiment in a doc- ument about a product. However, most of existing methods only focus on local text in- formation and ignore …...

Most previous approaches to Chinese word segmentation formalize this prob- lem as a character-based sequence label- ing task so that only contextual informa- tion within fixed sized local windows and …...

Technical writing in professional environ- ments, such as user manual authoring, re- quires the use of uniform language. Non- uniform language detection is a novel task, which aims to guarantee …...

In this paper, we address the task of language- independent, knowledge-lean and unsuper- vised compound splitting, which is an essen- tial component for many natural language pro- cessing tasks such …...

AbstractNeural network models have recently been applied to the task of automatic essay scoring, giving promising results. Existing work used recurrent neural networks and convolutional neural networks to model input …...

AbstractWith the explosive growth of Internet, more and more domain-specific environments appear, such as forums, blogs, MOOCs and etc. Domain-specific words appear in these areas and always play a critical …...

AbstractWe introduce a novel iterative approach for event coreference resolution that gradually builds event clusters by exploiting inter-dependencies among event mentions within the same chain as well as across event …...

AbstractWe address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly noisier and …...

AbstractIn this work, we study the problem of part-of-speech tagging for Tweets. In contrast to newswire articles, Tweets are usually informal and contain numerous out-of-vocabulary words. Moreover, there is a …...

AbstractWe study response selection for multi-turn conversation in retrieval based chatbots. Existing work either concatenates utterances in context or matches a response with a highly abstract context vector finally, which …...

Distributed word representations are used in many natural language processing tasks. When dealing with ambiguous words, it is de- sired to generate multi-sense embeddings, i.e., multiple representations per word. Therefore, …...

This paper introduces “Czech Text Document Corpus v 2.0”, a collection of text documents for automatic document classification in Czech language. It is composed of the text documents provided by …...

AbstractAspect-level sentiment analysis aims to identify the sentiment of a specific target in its context. Previous works have proved that the interactions between aspects and the contexts are important. On …...

AbstractWe propose a novel recurrent neural network-based approach to simultaneously handle nested named entity recognition and nested entity mention detection. The model learns a hypergraph representation for nested entities using …...

AbstractComplex questions in reading comprehension tasks require integrating information from multiple sentences. In this work, to answer such questions involving temporal and causal relations, we generate event graphs from text …...

AbstractNamed entity recognition (NER) in Chinese is essential but difficult because of the lack of natural delimiters. Therefore, Chinese Word Segmentation (CWS) is usually considered as the first step for …...

Due to the scarcity of parallel training data for many language pairs, quasi-parallel or comparable training data provides an important alternative resource for train- ing machine translation systems for such …...

AbstractOne key component in text-to-SQL is to predict the comparison relations between columns and their values. To the best of our knowledge, no existing models explicitly introduce external common knowledge …...

AbstractCurrently, researchers have paid great attention to retrieval-based dialogues in open-domain. In particular, people study the problem by investigating context-response matching for multi-turn response selection based on publicly recognized benchmark …...

AbstractOut-of-domain (OOD) detection for low-resource text classification is a realistic but understudied task. The goal is to detect the OOD cases with limited in-domain (ID) training data, since in machine …...

AbstractText infilling aims at filling in the missing part of a sentence or paragraph, which has been applied to a variety of real-world natural language generation scenarios. Given a well-trained …...

AbstractWe present a new summarisation task, taking scientific articles and producing journal table-of-contents entries in the chemistry domain. These are one- or two-sentence author-written summaries that present the key findings …...

AbstractKnowledge Graph Completion (KGC) aims at automatically predicting missing links for large-scale knowledge graphs. A vast number of state-of-the-art KGC techniques have got published at top conferences in several research …...

AbstractCross-domain sentiment classification aims to address the lack of massive amounts of labeled data. It demands to predict sentiment polarity on a target domain utilizing a classifier learned from a …...

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 …...

AbstractWe propose an end-to-end approach for synthetic QA data generation. Our model comprises a single transformer-based encoder-decoder network that is trained end-to-end to generate both answers and questions. In a …...

AbstractAutomated fact extraction and verification is a challenging task that involves finding relevant evidence sentences from a reliable corpus to verify the truthfulness of a claim. Existing models either (i) …...

AbstractAlthough deep neural networks are effective at extracting high-level features, classification methods usually encode an input into a vector representation via simple feature aggregation operations (e.g. pooling). Such operations limit …...

AbstractDetecting cybersecurity events is necessary to keep us informed about the fast growing number of such events reported in text. In this work, we focus on the task of event …...

AbstractBalancing accuracy and latency is a great challenge for simultaneous translation. To achieve high accuracy, the model usually needs to wait for more streaming text before translation, which results in …...

AbstractConcept graphs are created as universal taxonomies for text understanding in the open-domain knowledge. The nodes in concept graphs include both entities and concepts. The edges are from entities to …...

AbstractPhysical common sense plays an essential role in the cognition abilities of robots for human-robot interaction. Machine learning methods have shown promising results on physical commonsense learning in natural language …...

AbstractThe human language can be expressed through multiple sources of information known as modalities, including tones of voice, facial gestures, and spoken language. Recent multimodal learning with strong performances on …...

AbstractNon-task oriented dialogue systems have achieved great success in recent years due to largely accessible conversation data and the development of deep learning techniques. Given a context, current systems are …...

AbstractDetecting public sentiment drift is a challenging task due to sentiment change over time. Existing methods first build a classification model using historical data and subsequently detect drift if the …...

AbstractMath word problems solving remains a challenging task where potential semantic and mathematical logic need to be mined from natural language. Although previous researches employ the Seq2Seq technique to transform …...

AbstractSemantic hashing is a powerful paradigm for representing texts as compact binary hash codes. The explosion of short text data has spurred the demand of few-bits hashing. However, the performance …...

AbstractMuch research effort has been put to multilingual knowledge graph (KG) embedding methods to address the entity alignment task, which seeks to match entities in different languagespecific KGs that refer …...

In this paper, we consider a realistic scenario on stance detection with more application po- tential, i.e., zero-shot and few-shot stance de- tection, which identifies stances for a wide range …...

AbstractSemantic textual similarity (STS) systems estimate the degree of the meaning similarity between two sentences. Cross-lingual STS systems estimate the degree of the meaning similarity between two sentences, each in …...

AbstractFake news articles often stir the readers’ attention by means of emotional appeals that arouse their feelings. Unlike in short news texts, authors of longer articles can exploit such affective …...

AbstractIdentifying named entities in written text is an essential component of the text processing pipeline used in applications such as text editors to gain a better understanding of the semantics …...

Document-level relation extraction has at- tracted much attention in recent years. It is usu- ally formulated as a classification problem that predicts relations for all entity pairs in the doc- …...

AbstractExisting text style transfer (TST) methods rely on style classifiers to disentangle the text’s content and style attributes for text style transfer. While the style classifier plays a critical role …...

AbstractModelling and understanding dialogues in a conversation depends on identifying the user intent from the given text. Unknown or new intent detection is a critical task, as in a realistic …...

AbstractAspect Sentiment Triplet Extraction (ASTE) aims to extract sentiment triplets from sentences, which was recently formalized as an effective machine reading comprehension (MRC) based framework. However, when facing multiple aspect …...

AbstractWe investigate Referring Image Segmentation (RIS), which outputs a segmentation map corresponding to the natural language description. Addressing RIS efficiently requires considering the interactions happening across visual and linguistic modalities …...

AbstractRecently, Bert-based models have dominated the research of Chinese spelling correction (CSC). These methods have two limitations: (1) they have poor performance on multi-typo texts. In such texts, the context …...

AbstractMedical report automatic generation has gained increasing interest recently as a way to help radiologists write reports more efficiently. However, this image-to-text task is rather challenging due to the typical …...

AbstractRecent advances in large-scale pre-training provide large models with the potential to learn knowledge from the raw text. It is thus natural to ask whether it is possible to leverage …...

AbstractEvent extraction (EE) is one of the fundamental tasks for information extraction whose goal is to identify mentions of events and their participants in text. Due to its importance, different …...

AbstractKnowledge-grounded dialogue systems are challenging to build due to the lack of training data and heterogeneous knowledge sources. Existing systems perform poorly on unseen topics due to limited topics covered …...

AbstractFew-shot knowledge graph completion (FKGC) aims to infer unknown fact triples of a relation using its few-shot reference entity pairs. Recent FKGC studies focus on learning semantic representations of entity …...

AbstractCurrent methods for few-shot fine-tuning of pretrained masked language models (PLMs) require carefully engineered prompts and verbalizers for each new task to convert examples into a cloze-format that the PLM …...

AbstractRecent advancements in pre-trained language models (PLMs) have demonstrated that these models possess some degree of syntactic awareness. To leverage this knowledge, we propose a novel chart-based method for extracting …...

AbstractNegation scope resolution is the process of detecting the negated part of a sentence. Unlike the syntax-based approach employed in previous research, state-of-the-art methods performed better without the explicit use …...

AbstractLarge-scale pre-trained language models (PLMs) bring new opportunities to challenging problems, especially those that need high-level intelligence, such as the math word problem (MWPs). However, directly applying existing PLMs to …...

AbstractA crucial issue of current text generation models is that they often uncontrollably generate text that is factually inconsistent with inputs. Due to lack of annotated data, existing factual consistency …...