Benchmark Dataset

In this paper we describe the application of Memory-Based Learning to the problem of Prepositional Phrase attachment disam- biguation. We compare Memory-Based Learning, which stores examples in mem- ory and …...

With the increasing use of research paper search engines, such as CiteSeer, for both lit- erature search and hiring decisions, the accu- racy of such systems is of paramount impor- …...

We introduce a relation extraction method to identify the sentences in biomedical text that indicate an interaction among the protein names mentioned. Our approach is based on the analysis of …...

Question classification plays an important role in question answering. Features are the key to obtain an accurate question classifier. In con- trast to Li and Roth (2002)’s approach which makes …...

Sense induction seeks to automatically identify word senses directly from a cor- pus. A key assumption underlying pre- vious work is that the context surround- ing an ambiguous word is …...

A number of recent articles in computational linguistics venues called for a closer exami- nation of the type of noise present in anno- tated datasets used for benchmarking (Rei- dsma …...

We describe a sentiment classification method that is applicable when we do not have any la- beled data for a target domain but have some labeled data for multiple other …...

In this paper, we investigate a full-fledged supervised machine learning framework for identifying English phrasal verbs in a given context. We concentrate on those that we de- fine as the …...

In this paper, we study the answer sentence selection problem for ques- tion answering. Unlike previous work, which primarily leverages syntactic analy- sis through dependency tree matching, we focus on …...

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 …...

Implicit discourse relation recognition re- mains a serious challenge due to the ab- sence of discourse connectives. In this pa- per, we propose a Shallow Convolutional Neural Network (SCNN) for …...

Engaging in a debate with oneself or others to take decisions is an integral part of our day-to- day life. A debate on a topic (say, use of per- formance …...

State of the art in opinion mining mainly focuses on positive and negative senti- ment summarisation of online customer reviews. We observe that reviewers tend to provide advice, recommendations and …...

Most of the state-of-the-art sentiment classifi- cation methods are based on supervised learn- ing algorithms which require large amounts of manually labeled data. However, the labeled resources are usually imbalanced …...

AbstractTarget-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences on determining the sentiment …...

Domain adaptation is an important re- search topic in sentiment analysis area. Existing domain adaptation methods usu- ally transfer sentiment knowledge from only one source domain to target do- main. …...

AbstractIntegrating text and knowledge into a unified semantic space has attracted significant research interests recently. However, the ambiguity in the common space remains a challenge, namely that the same mention …...

AbstractRecently emerged intelligent assistants on smartphones and home electronics (e.g., Siri and Alexa) can be seen as novel hybrids of domain-specific task-oriented spoken dialogue systems and open-domain non-task-oriented ones. To …...

AbstractWe introduce globally normalized convolutional neural networks for joint entity classification and relation extraction. In particular, we propose a way to utilize a linear-chain conditional random field output layer for …...

AbstractRecent work has addressed the problem of detecting relevant claims for a given controversial topic. We introduce the complementary task of Claim Stance Classification, along with the first benchmark dataset …...

Word problem solving has always been a challenging task as it involves reasoning across sentences, identification of operations and their order of application on relevant operands. Most of the earlier …...

AbstractMotivated by recent findings on the probabilistic modeling of acceptability judgments, we propose syntactic log-odds ratio (SLOR), a normalized language model score, as a metric for referenceless fluency evaluation of …...

AbstractIn this paper, we propose a novel framework for sarcasm generation; the system takes a literal negative opinion as input and translates it into a sarcastic version. Our framework does …...

AbstractMultilingual knowledge graphs (KGs), such as YAGO and DBpedia, represent entities in different languages. The task of cross-lingual entity alignment is to match entities in a source language with their …...

AbstractDuring the past few decades, knowledge bases (KBs) have experienced rapid growth. Nevertheless, most KBs still suffer from serious incompletion. Researchers proposed many tasks such as knowledge base completion and …...

AbstractWe propose a distance supervised relation extraction approach for long-tailed, imbalanced data which is prevalent in real-world settings. Here, the challenge is to learn accurate “few-shot” models for classes existing …...

AbstractOpen Information Extraction (OpenIE), the problem of harvesting triples from natural language text whose predicate relations are not aligned to any pre-defined ontology, has been a popular subject of research …...

AbstractLack of labelled data in the target domain for training is a common problem in domain adaptation. To overcome this problem, we propose a novel unsupervised domain adaptation method that …...

AbstractApplications such as textual entailment, plagiarism detection or document clustering rely on the notion of semantic similarity, and are usually approached with dimension reduction techniques like LDA or with embedding-based …...

AbstractWe present the pilot SemEval task on Suggestion Mining. The task consists of subtasks A and B, where we created labeled data from feedback forum and hotel reviews respectively. Subtask …...

AbstractWe continue the study of generating se-mantically correct regular expressions from natural language descriptions (NL). The current state-of-the-art model SemRegex produces regular expressions from NLs by rewarding the reinforced learning …...

AbstractRecent work in neural generation has attracted significant interest in controlling the form of text, such as style, persona, and politeness. However, there has been less work on controlling neural …...

AbstractOver 97 million inhabitants speak Vietnamese as the native language in the world. However, there are few research studies on machine reading comprehension (MRC) in Vietnamese, the task of understanding …...

AbstractThe traditional approach of querying a relational database is via a formal language, namely SQL. Recent developments in the design of natural language interfaces to databases show promising results for …...

AbstractRecently there are increasing concerns about the fairness of Artificial Intelligence (AI) in real-world applications such as computer vision and recommendations. For example, recognition algorithms in computer vision are unfair …...

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 …...

AbstractIn this paper, we present Chinese lexical fusion recognition, a new task which could be regarded as one kind of coreference recognition. First, we introduce the task in detail, showing …...

AbstractNeural network (NN) based data2text models achieve state-of-the-art (SOTA) performance in most metrics, but they sometimes drop or modify the information in the input, and it is hard to control …...

AbstractThere is little to no data available to build natural language processing models for most endangered languages. However, textual data in these languages often exists in formats that are not …...

AbstractStory generation is a challenging task of automatically creating natural languages to describe a sequence of events, which requires outputting text with not only a consistent topic but also novel …...

AbstractHoping for a large number of clicks and potentially high social shares, journalists of various news media outlets publish sensationalist headlines on social media. These headlines lure the readers to …...

AbstractIn recent years, parsing performance is dramatically improved on in-domain texts thanks to the rapid progress of deep neural network models. The major challenge for current parsing research is to …...

AbstractSplit and Rephrase is a text simplification task of rewriting a complex sentence into simpler ones. As a relatively new task, it is paramount to ensure the soundness of its …...

AbstractThe task of procedural text comprehension aims to understand the dynamic nature of entities/objects in a process. Here, the key is to track how the entities interact with each other …...

AbstractDomain adaption for word segmentation and POS tagging is a challenging problem for Chinese lexical processing. Self-training is one promising solution for it, which struggles to construct a set of …...

We propose a new task and dataset for a common problem in social science research: “upsampling” coarse document labels to fine- grained labels or spans. We pose the problem in …...

AbstractDocument-level MT models are still far from satisfactory. Existing work extend translation unit from single sentence to multiple sentences. However, study shows that when we further enlarge the translation unit …...

AbstractComputer-aided translation (CAT), the use of software to assist a human translator in the translation process, has been proven to be useful in enhancing the productivity of human translators. Autocompletion, …...

AbstractImplicit sentiment analysis, aiming at detecting the sentiment of a sentence without sentiment words, has become an attractive research topic in recent years. In this paper, we focus on event-centric …...

Interactive argument pair identification is es- sential in the context of dialogical argumenta- tion mining. Existing research treats it as a problem of sentence matching and largely re- lies on …...

AbstractIn this paper, we study the importance of context in predicting the citation worthiness of sentences in scholarly articles. We formulate this problem as a sequence labeling task solved using …...

AbstractMedical code prediction from clinical notes aims at automatically associating medical codes with the clinical notes. Rare code problem, the medical codes with low occurrences, is prominent in medical code …...

AbstractRecognizing offensive text is an important requirement for every content management system, especially for social networks. While the majority of the prior work formulate this problem as text classification, i.e., …...

AbstractThis work demonstrates that Legal Judgement Prediction systems without expert-informed adjustments can be vulnerable to shallow, distracting surface signals that arise from corpus construction, case distribution, and confounding factors. To …...

AbstractNatural language spatial video grounding aims to detect the relevant objects in video frames with descriptive sentences as the query. In spite of the great advances, most existing methods rely …...

AbstractApplying existing methods to emotional support conversation—which provides valuable assistance to people who are in need—has two major limitations: (a) they generally employ a conversation-level emotion label, which is too …...

AbstractIn a citation graph, adjacent paper nodes share related scientific terms and topics. The graph thus conveys unique structure information of document-level relatedness that can be utilized in the paper …...

AbstractRecent work has introduced an important yet relatively under-explored NLP task called Semantic Overlap Summarization (SOS) that entails generating a summary from multiple alternative narratives which conveys the common information …...

AbstractSense embedding learning methods learn different embeddings for the different senses of an ambiguous word. One sense of an ambiguous word might be socially biased while its other senses remain …...

AbstractAbstractive dialogue summarization aims to convert a long dialogue content into its short form where the salient information is preserved while the redundant pieces are ignored. Different from the well-structured …...

AbstractImage-to-text tasks such as open-ended image captioning and controllable image description have received extensive attention for decades. Here we advance this line of work further, presenting Visual Spatial Description (VSD), …...

AbstractTranslation suggestion (TS), which provides alternatives for specific words or phrases given the entire documents generated by machine translation (MT), has been proven to play a significant role in post-editing …...

AbstractFor a natural language understanding benchmark to be useful in research, it has to consist of examples that are diverse and difficult enough to discriminate among current and near-future state-of-the-art …...

AbstractEvent Detection (ED) aims to identify mentions/triggers of real world events in text. In the literature, this task is modeled as a sequence-labeling or word-prediction problem. In this work, we …...

AbstractWhen people think of everyday things like an egg, they typically have a mental image associated with it. This allows them to correctly judge, for example, that “the yolk surrounds …...

AbstractContext information modeling is an important task in conversational KBQA. However, existing methods usually assume the independence of utterances and model them in isolation. In this paper, we propose a …...

AbstractImage translation is a task that translates an image containing text in the source language to the target language. One major challenge with image translation is the modality gap between …...