Attention

We propose a simple neural architecture for nat- ural language inference. Our approach uses at- tention to decompose the problem into subprob- lems that can be solved separately, thus making …...

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 …...

AbstractSememes are minimum semantic units of word meanings, and the meaning of each word sense is typically composed by several sememes. Since sememes are not explicit for each word, people …...

This paper describes SYSTRAN’s sys- tems submitted to the WMT 2017 shared news translation task for English-German, in both translation directions. Our sys- tems are built using OpenNMT1, an open- …...

This paper describes the Neural Machine Translation systems of Xiamen University for the translation tasks of WMT 17. Our systems are based on the Encoder-Decoder framework with attention. We partici- …...

AbstractWe investigate how encoder-decoder models trained on a synthetic dataset of task-oriented dialogues process disfluencies, such as hesitations and self-corrections. We find that, contrary to earlier results, disfluencies have very …...

AbstractExisting keyphrase extraction methods suffer from data sparsity problem when they are conducted on short and informal texts, especially microblog messages. Enriching context is one way to alleviate this problem. …...

AbstractIn this paper, we focus on quantifying model stability as a function of random seed by investigating the effects of the induced randomness on model performance and the robustness of …...

AbstractRecently, there has been a great interest in the development of small and accurate neural networks that run entirely on devices such as mobile phones, smart watches and IoT. This …...

AbstractWe propose a new end-to-end model that treats AMR parsing as a series of dual decisions on the input sequence and the incrementally constructed graph. At each time step, our …...

AbstractAttention is a key component of Transformers, which have recently achieved considerable success in natural language processing. Hence, attention is being extensively studied to investigate various linguistic capabilities of Transformers, …...

AbstractProblems involving code-mixed language are often plagued by a lack of resources and an absence of materials to perform sophisticated transfer learning with. In this paper we describe our submission …...

AbstractAttention has been proven successful in many natural language processing (NLP) tasks. Recently, many researchers started to investigate the interpretability of attention on NLP tasks. Many existing approaches focused on …...

AbstractPooling-based recurrent neural architectures consistently outperform their counterparts without pooling on sequence classification tasks. However, the reasons for their enhanced performance are largely unexamined. In this work, we examine three …...

AbstractIt is popular that neural graph-based models are applied in existing aspect-based sentiment analysis (ABSA) studies for utilizing word relations through dependency parses to facilitate the task with better semantic …...

AbstractPre-trained language models have shown stellar performance in various downstream tasks. But, this usually comes at the cost of high latency and computation, hindering their usage in resource-limited settings. In …...

AbstractTo improve computer-based recognition from video of isolated signs from American Sign Language (ASL), we propose a new skeleton-based method that involves explicit detection of the start and end frames …...

AbstractThe performance of deep learning models in NLP and other fields of machine learning has led to a rise in their popularity, and so the need for explanations of these …...

AbstractAfter its introduction the Transformer architecture quickly became the gold standard for the task of neural machine translation. A major advantage of the Transformer compared to previous architectures is the …...

AbstractIdioms such as “call it a day” and “piece of cake,” are prevalent in natural language. How do Transformer language models process idioms? This study examines this question by analysing …...