Deep learning approaches have achieved great success in the field of Natural Language
Processing (NLP). However, directly training deep neural models often suffer from overfitting …

Transformer-based architectures brought a breeze of change to Word Sense
Disambiguation (WSD), improving models' performances by a large margin. The fast …

The data imbalance problem is a crucial issue for the multi-label text classification. Some
existing works tackle it by proposing imbalanced loss objectives instead of the vanilla cross …

Over the past few years, Word Sense Disambiguation (WSD) has received renewed interest:
recently proposed systems have shown the remarkable effectiveness of deep learning …

Automatic prediction on the peer-review aspect scores of academic papers can be a useful
assistant tool for both reviewers and authors. To handle the small size of published datasets …

With the rapid development of the Internet, malicious domain names pose more and more
serious threats to many fields, such as network security and social security, and there have …

Local coherence relation between two phrases/sentences such as cause-effect and contrast
gives a strong influence of whether a text is well-structured or not. This paper follows the …

Lexical ambiguity is one of the many challenging linguistic phenomena involved in
translation, ie, translating an ambiguous word with its correct sense. In this respect, previous …

Deep learning models such as RoBERTa and Bi-LSTM are widely utilized in user intention
classification tasks. However, in the medical field, there are difficulties in recognizing user …

The performance of NMT systems has improved drastically in the past few years but the
translation of multi-sense words still poses a challenge. Since word senses are not …