Pre-trained Language Models (PLMs) which are trained on large text corpus via self-
supervised learning method, have yielded promising performance on various tasks in …

Non-autoregressive (NAR) generation, which is first proposed in neural machine translation
(NMT) to speed up inference, has attracted much attention in both machine learning and …

Few-shot Named Entity Recognition (NER) aims to identify named entities with very little
annotated data. Previous methods solve this problem based on token-wise classification …

Few-shot question answering (QA) aims at precisely discovering answers to a set of
questions from context passages while only a few training samples are available. Although …

In recent years, Graph Neural Networks (GNNs) and Large Language Models (LLMs) have
exhibited remarkable capability in addressing different graph learning and natural language …

Extractive question answering is one of the most important tasks in natural language
processing (NLP) which has high research value. In order to sort out its development …

Few-shot extractive question answering (EQA) is a challenging task in natural language
processing, whose current methods are mainly based on pretrained language models …

Event argument extraction (EAE) aims to identify the spans and roles of arguments for the
given event type. Deep learning-based EAE methods, especially generation-based …

The rapid development of pre-trained language models (PLMs) has significantly enhanced
the performance of machine reading comprehension (MRC). Nevertheless, the traditional …

Distant Supervision (DS) is a promising learning approach for MRC by leveraging easily-
obtained question-answer pairs. Unfortunately, the heuristically annotated dataset will …