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In the last several years, neural network models have significantly improved accu- racy in a number of NLP tasks. How- ever, one serious drawback that has im- peded their adoption …...

A growing body of research has recently been conducted on semantic textual similarity using a variety of neural network models. While re- cent research focuses on word-based represen- tation for …...

A common problem in cognitive modelling is lack of access to accurate broad-coverage models of event-level surprisal. As shown in, e.g., Bicknell et al. (2010), event-level knowl- edge does affect …...

We introduce a new methodology for in- trinsic evaluation of word representations. Specifically, we identify four fundamen- tal criteria based on the characteristics of natural language that pose difficulties to …...

The problem of rare and unknown words is an important issue that can potentially effect the performance of many NLP sys- tems, including traditional count-based and deep learning models. We …...

AbstractWe investigate the value of feature engineering and neural network models for predicting successful writing. Similar to previous work, we treat this as a binary classification task and explore new …...

AbstractInferring implicit discourse relations in natural language text is the most difficult subtask in discourse parsing. Many neural network models have been proposed to tackle this problem. However, the comparison …...

AbstractThis paper deals with sentence-level sentiment classification. Though a variety of neural network models have been proposed recently, however, previous models either depend on expensive phrase-level annotation, most of which …...

AbstractIn this paper, we address two different types of noise in information extraction models: noise from distant supervision and noise from pipeline input features. Our target tasks are entity typing …...

AbstractClassification tasks are usually analysed and improved through new model architectures or hyperparameter optimisation but the underlying properties of datasets are discovered on an ad-hoc basis as errors occur. However, …...

AbstractThe field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new models have been …...

AbstractDetecting emotion from dialogue is a challenge that has not yet been extensively surveyed. One could consider the emotion of each dialogue turn to be independent, but in this paper, …...

AbstractRecent progress in hardware and methodology for training neural networks has ushered in a new generation of large networks trained on abundant data. These models have obtained notable gains in …...

AbstractNeural network models have been actively applied to word segmentation, especially Chinese, because of the ability to minimize the effort in feature engineering. Typical segmentation models are categorized as character-based, …...

AbstractContextual word representations derived from large-scale neural language models are successful across a diverse set of NLP tasks, suggesting that they encode useful and transferable features of language. To shed …...

AbstractOpen-domain question answering (OpenQA) aims to answer questions through text retrieval and reading comprehension. Recently, lots of neural network-based models have been proposed and achieved promising results in OpenQA. However, …...

AbstractWe present a large-scale corpus of e-mail conversations with domain-agnostic and two-level dialogue act (DA) annotations towards the goal of a better understanding of asynchronous conversations. We annotate over 6,000 …...

AbstractThe understanding of a text by a reader or listener is conditioned by the adequacy of the text’s characteristics with the person’s capacities and knowledge. This adequacy is critical in …...

AbstractGiven the fast development of analysis techniques for NLP and speech processing systems, few systematic studies have been conducted to compare the strengths and weaknesses of each method. As a …...

AbstractNeural network-based models augmented with unsupervised pre-trained knowledge have achieved impressive performance on text summarization. However, most existing evaluation methods are limited to an in-domain setting, where summarizers are trained …...

AbstractThe success of the large neural language models on many NLP tasks is exciting. However, we find that these successes sometimes lead to hype in which these models are being …...

AbstractOne of the remaining challenges for aspect term extraction in sentiment analysis resides in the extraction of phrase-level aspect terms, which is non-trivial to determine the boundaries of such terms. …...

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 …...

AbstractLearners that are exposed to the same training data might generalize differently due to differing inductive biases. In neural network models, inductive biases could in theory arise from any aspect …...

AbstractThe ability of common sense validation and explanation is very important for most models. Most obviously, this will directly affect the rationality of the generated model output. The large amount …...

AbstractTechnical logbooks are a challenging and under-explored text type in automated event identification. These texts are typically short and written in non-standard yet technical language, posing challenges to off-the-shelf NLP …...

AbstractFor natural language processing systems, two kinds of evidence support the use of text representations from neural language models “pretrained” on large unannotated corpora: performance on application-inspired benchmarks (Peters et …...

AbstractSequence-to-sequence transduction is the core problem in language processing applications as diverse as semantic parsing, machine translation, and instruction following. The neural network models that provide the dominant solution to …...

Syntactic and semantic structure directly re- flect relations expressed by the text at hand and are thus very useful for the relation extrac- tion (RE) task. Their symbolic nature allows …...

AbstractNeural language models have achieved superior performance. However, these models also suffer from the pathology of overconfidence in the out-of-distribution examples, potentially making the model difficult to interpret and making …...