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This paper examines how a new class of nonparametric Bayesian models can be ef- fectively applied to an open-domain event coreference task. Designed with the pur- pose of clustering complex …...

In this paper we introduce some of the key NLP-related problems related to the practice of Evidence Based Medicine and propose the task of multi-document query-focused sum- marisation as a …...

We present a novel approach to grammatical error correction based on Alternating Struc- ture Optimization. As part of our work, we introduce the NUS Corpus of Learner En- glish (NUCLE), …...

We introduce a novel machine learning frame- work based on recursive autoencoders for sentence-level prediction of sentiment label distributions. Our method learns vector space representations for multi-word phrases. In sentiment …...

Many types of polysemy are not word specific, but are instances of general sense alternations such as ANIMAL-FOOD. Despite their perva- siveness, regular alternations have been mostly ignored in empirical …...

We compare two different methods in domain adaptation applied to constituent parsing: parser combination and co- training, each used to transfer information from the source domain of news to the …...

Analyzing arguments in user-generated Web discourse has recently gained atten- tion in argumentation mining, an evolving field of NLP. Current approaches, which employ fully-supervised machine learn- ing, are usually domain …...

Discourse deixis is a linguistic phenomenon in which pronouns have verbal or clausal, rather than nominal, antecedents. Studies have esti- mated that between 5% and 10% of pronouns in non-conversational …...

AbstractOpinion Mining is a topic which attracted a lot of interest in the last years. By observing the literature, it is often hard to replicate system evaluation due to the …...

AbstractMost diacritics in Arabic represent short vowels. In Arabic orthography, such diacritics are considered optional. The absence of these diacritics naturally leads to significant word ambiguity to top the inherent …...

In this paper we consider several approaches to predicting semantic textual similarity us- ing word embeddings, as well as methods for forming embeddings for larger units of text. We compare …...

AbstractWe address the problem of end-to-end visual storytelling. Given a photo album, our model first selects the most representative (summary) photos, and then composes a natural language story for the …...

AbstractThere have been several attempts to define a plausible motivation for a chit-chat dialogue agent that can lead to engaging conversations. In this work, we explore a new direction where …...

AbstractWe present a neural architecture for modeling argumentative dialogue that explicitly models the interplay between an Opinion Holder’s (OH’s) reasoning and a challenger’s argument, with the goal of predicting if …...

AbstractAs a precious part of the human cultural heritage, Chinese poetry has influenced people for generations. Automatic poetry composition is a challenge for AI. In recent years, significant progress has …...

AbstractSummarizing a document requires identifying the important parts of the document with an objective of providing a quick overview to a reader. However, a long article can span several topics …...

AbstractThe quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality, and relevance of the training dataset. Such approaches have achieved promising results for bilingually …...

AbstractPrior work on controllable text generation usually assumes that the controlled attribute can take on one of a small set of values known a priori. In this work, we propose …...

AbstractThis paper presents computational approaches for automatically detecting critical plot twists in reviews of media products. First, we created a large-scale book review dataset that includes fine-grained spoiler annotations at …...

AbstractCross-lingual word embeddings (CLEs) facilitate cross-lingual transfer of NLP models. Despite their ubiquitous downstream usage, increasingly popular projection-based CLE models are almost exclusively evaluated on bilingual lexicon induction (BLI). Even …...

AbstractRhetoric is a vital element in modern poetry, and plays an essential role in improving its aesthetics. However, to date, it has not been considered in research on automatic poetry …...

AbstractBiomedical concepts are often mentioned in medical documents under different name variations (synonyms). This mismatch between surface forms is problematic, resulting in difficulties pertaining to learning effective representations. Consequently, this …...

AbstractWe present an approach to minimally supervised relation extraction that combines the benefits of learned representations and structured learning, and accurately predicts sentence-level relation mentions given only proposition-level supervision from …...

AbstractThe study of linguistic typology is rooted in the implications we find between linguistic features, such as the fact that languages with object-verb word ordering tend to have postpositions. Uncovering …...

AbstractThis paper describes a system that aims at assessing humour intensity in edited news headlines as part of the 7th task of SemEval-2020 on “Humor, Emphasis and Sentiment”. Various factors …...

AbstractEntity linking, as one of the fundamental tasks in natural language processing, is crucial to knowledge fusion, knowledge base construction and update. Nevertheless, in contrast to the research on entity …...

AbstractEntity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising performance by modeling …...

AbstractWhile recent advances in language modeling has resulted in powerful generation models, their generation style remains implicitly dependent on the training data and can not emulate a specific target style. …...

AbstractIn this work, we perform the first large-scale analysis of discourse in media dialog and its impact on generative modeling of dialog turns, with a focus on interrogative patterns and …...

AbstractMany important problems can be formulated as reasoning in knowledge graphs. Representation learning has proved extremely effective for transductive reasoning, in which one needs to make new predictions for already …...

AbstractSpoken Language Understanding infers semantic meaning directly from audio data, and thus promises to reduce error propagation and misunderstandings in end-user applications. However, publicly available SLU resources are limited. In …...

AbstractSyntactic and pragmatic completeness is known to be important for turn-taking prediction, but so far machine learning models of turn-taking have used such linguistic information in a limited way. In …...

AbstractDespite the number of currently available datasets on video-question answering, there still remains a need for a dataset involving multi-step and non-factoid answers. Moreover, relying on video transcripts remains an …...

AbstractVariational Neural Machine Translation (VNMT) is an attractive framework for modeling the generation of target translations, conditioned not only on the source sentence but also on some latent random variables. …...

AbstractExisting black box search methods have achieved high success rate in generating adversarial attacks against NLP models. However, such search methods are inefficient as they do not consider the amount …...

AbstractThe ability to generate clarification questions i.e., questions that identify useful missing information in a given context, is important in reducing ambiguity. Humans use previous experience with similar contexts to …...

AbstractEmpathetic dialog generation aims at generating coherent responses following previous dialog turns and, more importantly, showing a sense of caring and a desire to help. Existing models either rely on …...

AbstractIn this work, we investigate the problems of semantic parsing in a few-shot learning setting. In this setting, we are provided with k utterance-logical form pairs per new predicate. The …...

AbstractKnowledge Graph Embeddings (KGEs) have been intensively explored in recent years due to their promise for a wide range of applications. However, existing studies focus on improving the final model …...

AbstractGenerating metaphors is a challenging task as it requires a proper understanding of abstract concepts, making connections between unrelated concepts, and deviating from the literal meaning. In this paper, we …...

AbstractThis paper introduces Semantic Frame Forecast, a task that predicts the semantic frames that will occur in the next 10, 100, or even 1,000 sentences in a running story. Prior …...

AbstractThis paper proposes to study a fine-grained semantic novelty detection task, which can be illustrated with the following example. It is normal that a person walks a dog in the …...

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 …...

AbstractCharts are very popular to analyze data and convey important insights. People often analyze visualizations to answer open-ended questions that require explanatory answers. Answering such questions are often difficult and …...

AbstractRole-oriented dialogue summarization is to generate summaries for different roles in the dialogue, e.g., merchants and consumers. Existing methods handle this task by summarizing each role’s content separately and thus …...

AbstractWe present a novel pipeline for the collection of parallel data for the detoxification task. We collect non-toxic paraphrases for over 10,000 English toxic sentences. We also show that this …...

AbstractWith the availability of massive general-domain dialogue data, pre-trained dialogue generation appears to be super appealing to transfer knowledge from the general domain to downstream applications. In most existing work, …...

AbstractClass imbalance naturally exists when label distributions are not aligned across source and target domains. However, existing state-of-the-art UDA models learn domain-invariant representations across domains and evaluate primarily on class-balanced …...

AbstractPoetry generation, and creative language generation in general, usually suffers from the lack of large training data. In this paper, we present a novel framework to generate sonnets that does …...

AbstractThis work presents and evaluates an approach to efficiently leverage the context exploitation ability of pre-trained Transformer models as a way of boosting the performance of models tackling the Legal …...

AbstractIn-context learning, where pre-trained language models learn to perform tasks from task examples and instructions in their contexts, has attracted much attention in the NLP community. However, the ability of …...