23 citations · 59 across the 13 of their papers we have counts for
7 papers · 1 filter
A Large Language Model Enhanced Conversational Recommender System
Yue Feng, Shuchang Liu, Zhenghai Xue +5
Conversational recommender systems (CRSs) aim to recommend high-quality items to users through a dialogue interface. It usually contains multiple sub-tasks, such as user preference…
Unlocking the Potential of User Feedback: Leveraging Large Language Model as User Simulator to Enhance Dialogue System
Zhiyuan Hu, Yue Feng, Anh Tuan Luu +2
Dialogue systems and large language models (LLMs) have gained considerable attention. However, the direct utilization of LLMs as task-oriented dialogue (TOD) models has been found…
PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation
Zhiyuan Hu, Chumin Liu, Yue Feng +2
Controllable text generation is a challenging and meaningful field in natural language generation (NLG). Especially, poetry generation is a typical one with well-defined and strict…
Schema-Guided User Satisfaction Modeling for Task-Oriented Dialogues
Yue Feng, Yunlong Jiao, Animesh Prasad +3
User Satisfaction Modeling (USM) is one of the popular choices for task-oriented dialogue systems evaluation, where user satisfaction typically depends on whether the user's task g…
A Survey on Asking Clarification Questions Datasets in Conversational Systems
Hossein A. Rahmani, Xi Wang, Yue Feng +3
The ability to understand a user's underlying needs is critical for conversational systems, especially with limited input from users in a conversation. Thus, in such a domain, Aski…
Towards Asking Clarification Questions for Information Seeking on Task-Oriented Dialogues
Yue Feng, Hossein A. Rahmani, Aldo Lipani +1
Task-oriented dialogue systems aim at providing users with task-specific services. Users of such systems often do not know all the information about the task they are trying to acc…
A Graph-Guided Reasoning Approach for Open-ended Commonsense Question Answering
Zhen Han, Yue Feng, Mingming Sun
Recently, end-to-end trained models for multiple-choice commonsense question answering (QA) have delivered promising results. However, such question-answering systems cannot be dir…