In the era of deep learning, modeling for most natural language processing (NLP) tasks has
converged into several mainstream paradigms. For example, we usually adopt the …

Spatio-temporal video grounding aims to localize the aligned visual tube corresponding to a
language query. Existing techniques achieve such alignment by exploiting dense boundary …

Despite the success of ChatGPT, its performances on most NLP tasks are still well below the
supervised baselines. In this work, we looked into the causes, and discovered that its subpar …

Abstract Machine reading comprehension (MRC) is a challenging task in the field of artificial
intelligence. Most existing MRC works contain a semantic matching module, either explicitly …

Inspired by recent advances in retrieval augmented methods in NLP~\citep {
khandelwal2019generalization, khandelwal2020nearest, meng2021gnn}, in this paper, we …

Argument pair extraction (APE) aims to automatically mine argument pairs from two
interrelated argumentative documents. Existing studies typically identify argument pairs …

Existing studies typically handle aspect-based sentiment analysis by stacking multiple
neural modules, which inevitably result in severe error propagation. Instead, we propose a …

To better handle long-tail cases in the sequence labeling (SL) task, in this work, we
introduce graph neural networks sequence labeling (GNN-SL), which augments the vanilla …

Dependency parsing aims to extract syntactic dependency structure or semantic
dependency structure for sentences. Existing methods for dependency parsing include …

Semantic Role Labeling (SRL) aims at recognizing the predicate-argument structure of a
sentence and can be decomposed into two subtasks: predicate disambiguation and …