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AbstractTruly effective and practical educational systems will only be achievable when they have the ability to fully recognize deep relationships between a learner’s interpretation of a subject and the desired …...

AbstractThe paper describes our approach for SemEval-2018 Task 1: Affect Detection in Tweets. We perform experiments with manually compelled sentiment lexicons and word embeddings. We test their performance on twitter …...

AbstractPooling is an essential component of a wide variety of sentence representation and embedding models. This paper explores generalized pooling methods to enhance sentence embedding. We propose vector-based multi-head attention …...

AbstractNeural machine translation systems require a number of stacked layers for deep models. But the prediction depends on the sentence representation of the top-most layer with no access to low-level …...

AbstractAspect based sentiment analysis (ABSA) aims to identify the sentiment polarity towards the given aspect in a sentence, while previous models typically exploit an aspect-independent (weakly associative) encoder for sentence …...

AbstractSentence representation (SR) is the most crucial and challenging task in Machine Reading Comprehension (MRC). MRC systems typically only utilize the information contained in the sentence itself, while human beings …...

AbstractGenerating relevant responses in a dialog is challenging, and requires not only proper modeling of context in the conversation, but also being able to generate fluent sentences during inference. In …...

AbstractHuman-generated non-literal translations reflect the richness of human languages and are sometimes indispensable to ensure adequacy and fluency. Non-literal translations are difficult to produce even for human translators, especially for …...

AbstractAs the name implies, contextualized representations of language are typically motivated by their ability to encode context. Which aspects of context are captured by such representations? We introduce an approach …...

AbstractLearning high-quality sentence representations benefits a wide range of natural language processing tasks. Though BERT-based pre-trained language models achieve high performance on many downstream tasks, the native derived sentence representations …...

AbstractPretraining-based neural network models have demonstrated state-of-the-art (SOTA) performances on natural language processing (NLP) tasks. The most frequently used sentence representation for neural-based NLP methods is a sequence of subwords …...

Abstract“Learning sentence representation is a fundamental task in natural language processing and has been studied extensively. Recently, many works have obtained high-quality sentence representation based on contrastive learning from pre-trained …...

AbstractPrevious works have demonstrated the effectiveness of utilising pre-trained sentence encoders based on their sentence representations for meaning comparison tasks. Though such representations are shown to capture hidden syntax structures, …...

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 …...