Interactive Natural Language Processing (iNLP) has emerged as a novel paradigm within
the field of NLP, aimed at addressing limitations in existing frameworks while aligning with …

Even the most advanced language models remain susceptible to errors necessitating to
modify these models without initiating a comprehensive retraining process. Model editing …

Implicit Discourse Relation Recognition (IDRR) is a sophisticated and challenging task to
recognize the discourse relations between the arguments with the absence of discourse …

Question-answering systems are recognized as popular and frequently effective means of
information seeking on the web. In such systems, information seekers can receive a concise …

Aspect-Based Sentiment Analysis (ABSA) aims to provide fine-grained aspect-level
sentiment information. There are many ABSA tasks, and the current dominant paradigm is to …

Zero-shot learning is an approach where models generalize to unseen tasks without direct
training on them. We introduce the Unified Multiple-Choice (UniMC) framework, which is …

Prompt-tuning was proposed to bridge the gap between pretraining and downstream tasks,
and it has achieved promising results in Relation Extraction (RE). Although the existing …

Lifelong learning (LL) is an important ability for NLP models to learn new tasks continuously.
Architecture-based approaches are reported to be effective implementations for LL models …

Abstract In recent years, Graph Neural Network (GNN) approaches with enhanced
knowledge graphs (KG) perform well in question answering (QA) tasks. One critical …

Task generalization has been a long-standing challenge in Natural Language Processing
(NLP). Recent research attempts to improve the task generalization ability of pre-trained …