10 accepted papers
Language acquisition is vital to revealing the nature of human language intelligence and has recently emerged as a promising perspective for improving the interpretability of large language models (LLMs). However, it is ethically and practically infeasible to conduct experiments that require control
Spoken-only languages are languages without a writing system. They remain excluded from modern Natural Language Processing (NLP) advancements like Large Language Models (LLMs) due to their lack of textual data. Existing NLP research focuses primarily on high-resource or written low-resource language
Knowledge retrieval and response generation are fundamental to task-oriented dialogue systems. However, dialogue context frequently contains noisy or irrelevant information, leading to sub-optimal result in knowledge retrieval. One possible approach to retrieving knowledge is to manually annotate st…
Large language models (LLMs) fundamentally reshape learning and teaching models, shifting tutoring systems from supporting individual learning to facilitating collaborative learning (CL) like task-oriented group discussions. However, existing AI tutors struggle to guide CL, as they seldom model the
Task-oriented dialogue systems have recently gained increasing attention due to their capability of using natural language to fulfill specific user demands, such as restaurant reservation and hotel booking. Recent works directly model task-oriented dialogue response as a text generation task. Howeve…
This paper investigates the group characteristics in multi-agent collaborative systems under adversarial attacks. Adversarial agents are tasked with generating counterfactual answers to a given collaborative problem, while collaborative agents normally interact with other agents to solve the given p
Visual Word Sense Disambiguation (Visual-WSD), as a sub-task of fine-grained image-text retrieval, requires a high level of language-vision understanding to capture and exploit the nuanced relationships between text and visual features. However, the cross-linguistic background only with limited cont…
Word sense disambiguation (WSD) can be viewed as two subtasks: textual word sense disambiguation (Textual-WSD) and visual word sense disambiguation (Visual-WSD). They aim to identify the most semantically relevant senses or images to a given context containing ambiguous target words. However, existi…
Given the intricacy and variability of anatomical structures in medical images, some methods employ shape priors to constrain segmentation. However, limited by the representational capability of these priors, existing approaches often struggle to capture diverse target structure morphologies. To add…
Pretrained language models such as BERT have been successfully applied to a wide range of natural language processing tasks and also achieved impressive performance in document reranking tasks. Recent works indicate that further pretraining the language models on the task-specific datasets before fi…