Dependency trees have been intensively used with graph neural networks for aspect-based
sentiment classification. Though being effective, such methods rely on external dependency …

Recently, many pre-trained language models for source code have been proposed to model
the context of code and serve as a basis for downstream code intelligence tasks such as …

Head-driven phrase structure grammar (HPSG) enjoys a uniform formalism representing rich
contextual syntactic and even semantic meanings. This paper makes the first attempt to …

Syntactic and semantic parsing has been investigated for decades, which is one primary
topic in the natural language processing community. This article aims for a brief survey on …

Code models have made significant advancements in code intelligence by encoding
knowledge about programming languages. While previous studies have explored the …

Most graph neural networks follow the message passing mechanism. However, it faces the
over-smoothing problem when multiple times of message passing is applied to a graph …

With the recent success and popularity of pre-trained language models (LMs) in natural
language processing, there has been a rise in efforts to understand their inner workings. In …

Attention mechanisms have improved the performance of NLP tasks while allowing models
to remain explainable. Self-attention is currently widely used, however interpretability is …

Estimating probability distribution is one of the core issues in the NLP field. However, in both
deep learning (DL) and pre-DL eras, unlike the vast applications of linear-chain CRF in …

Constituency parsing and nested named entity recognition (NER) are similar tasks since
they both aim to predict a collection of nested and non-crossing spans. In this work, we cast …