59 citations · 241 across the 30 of their papers we have counts for
26 papers · 1 filter
CoAD: Automatic Diagnosis through Symptom and Disease Collaborative Generation
Huimin Wang, Wai-Chung Kwan, Kam-Fai Wong +1
Automatic diagnosis (AD), a critical application of AI in healthcare, employs machine learning techniques to assist doctors in gathering patient symptom information for precise dis…
UniTRec: A Unified Text-to-Text Transformer and Joint Contrastive Learning Framework for Text-based Recommendation
Zhiming Mao, Huimin Wang, Yiming Du +1
Prior study has shown that pretrained language models (PLM) can boost the performance of text-based recommendation. In contrast to previous works that either use PLM to encode user…
Towards Robust Personalized Dialogue Generation via Order-Insensitive Representation Regularization
Liang Chen, Hongru Wang, Yang Deng +3
Generating persona consistent dialogue response is important for developing an intelligent conversational agent. Recent works typically fine-tune large-scale pre-trained models on…
KGA: A General Machine Unlearning Framework Based on Knowledge Gap Alignment
Lingzhi Wang, Tong Chen, Wei Yuan +3
Recent legislation of the "right to be forgotten" has led to the interest in machine unlearning, where the learned models are endowed with the function to forget information about…
Enhancing Large Language Models Against Inductive Instructions with Dual-critique Prompting
Rui Wang, Hongru Wang, Fei Mi +4
Numerous works are proposed to align large language models (LLMs) with human intents to better fulfill instructions, ensuring they are trustful and helpful. Nevertheless, some huma…
Strategize Before Teaching: A Conversational Tutoring System with Pedagogy Self-Distillation
Lingzhi Wang, Mrinmaya Sachan, Xingshan Zeng +1
Conversational tutoring systems (CTSs) aim to help students master educational material with natural language interaction in the form of a dialog. CTSs have become a key pillar in…
DIGAT: Modeling News Recommendation with Dual-Graph Interaction
Zhiming Mao, Jian Li, Hongru Wang +2
News recommendation (NR) is essential for online news services. Existing NR methods typically adopt a news-user representation learning framework, facing two potential limitations.…
Improving Conversational Recommender System via Contextual and Time-Aware Modeling with Less Domain-Specific Knowledge
Lingzhi Wang, Shafiq Joty, Wei Gao +2
Conversational Recommender Systems (CRS) has become an emerging research topic seeking to perform recommendations through interactive conversations, which generally consist of gene…
A Survey on Recent Advances and Challenges in Reinforcement Learning Methods for Task-Oriented Dialogue Policy Learning
Wai-Chung Kwan, Hongru Wang, Huimin Wang +1
Dialogue Policy Learning is a key component in a task-oriented dialogue system (TDS) that decides the next action of the system given the dialogue state at each turn. Reinforcement…
Integrating Pretrained Language Model for Dialogue Policy Learning
Hongru Wang, Huimin Wang, Zezhong Wang +1
Reinforcement Learning (RL) has been witnessed its potential for training a dialogue policy agent towards maximizing the accumulated rewards given from users. However, the reward c…
RecInDial: A Unified Framework for Conversational Recommendation with Pretrained Language Models
Lingzhi Wang, Huang Hu, Lei Sha +3
Conversational Recommender System (CRS), which aims to recommend high-quality items to users through interactive conversations, has gained great research interest recently. A CRS i…
Neural News Recommendation with Collaborative News Encoding and Structural User Encoding
Zhiming Mao, Xingshan Zeng, Kam-Fai Wong
Automatic news recommendation has gained much attention from the academic community and industry. Recent studies reveal that the key to this task lies within the effective represen…
Prior Omission of Dissimilar Source Domain(s) for Cost-Effective Few-Shot Learning
Zezhong Wang, Hongru Wang, Kwan Wai Chung +3
Few-shot slot tagging is an emerging research topic in the field of Natural Language Understanding (NLU). With sufficient annotated data from source domains, the key challenge is h…
TopicRefine: Joint Topic Prediction and Dialogue Response Generation for Multi-turn End-to-End Dialogue System
Hongru Wang, Mingyu Cui, Zimo Zhou +2
A multi-turn dialogue always follows a specific topic thread, and topic shift at the discourse level occurs naturally as the conversation progresses, necessitating the model's abil…
Re-entry Prediction for Online Conversations via Self-Supervised Learning
Lingzhi Wang, Xingshan Zeng, Huang Hu +2
In recent years, world business in online discussions and opinion sharing on social media is booming. Re-entry prediction task is thus proposed to help people keep track of the dis…
Continuity of Topic, Interaction, and Query: Learning to Quote in Online Conversations
Lingzhi Wang, Jing Li, Xingshan Zeng +2
Quotations are crucial for successful explanations and persuasions in interpersonal communications. However, finding what to quote in a conversation is challenging for both humans…
Quotation Recommendation and Interpretation Based on Transformation from Queries to Quotations
Lingzhi Wang, Xingshan Zeng, Kam-Fai Wong
To help individuals express themselves better, quotation recommendation is receiving growing attention. Nevertheless, most prior efforts focus on modeling quotations and queries se…
KddRES: A Multi-level Knowledge-driven Dialogue Dataset for Restaurant Towards Customized Dialogue System
Hongru Wang, Min Li, Zimo Zhou +2
Compared with CrossWOZ (Chinese) and MultiWOZ (English) dataset which have coarse-grained information, there is no dataset which handle fine-grained and hierarchical level informat…
Fast and Scalable Dialogue State Tracking with Explicit Modular Decomposition
Dingmin Wang, Chenghua Lin, Qi Liu +1
We present a fast and scalable architecture called Explicit Modular Decomposition (EMD), in which we incorporate both classification-based and extraction-based methods and design f…
Joint Effects of Context and User History for Predicting Online Conversation Re-entries
Xingshan Zeng, Jing Li, Lu Wang +1
As the online world continues its exponential growth, interpersonal communication has come to play an increasingly central role in opinion formation and change. In order to help us…
A Joint Model of Conversational Discourse and Latent Topics on Microblogs
Jing Li, Yan Song, Zhongyu Wei +1
Conventional topic models are ineffective for topic extraction from microblog messages, because the data sparseness exhibited in short messages lacking structure and contexts resul…
Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learning
Baolin Peng, Xiujun Li, Jianfeng Gao +3
Training a task-completion dialogue agent via reinforcement learning (RL) is costly because it requires many interactions with real users. One common alternative is to use a user s…
Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning
Baolin Peng, Xiujun Li, Jianfeng Gao +3
This paper presents a new method --- adversarial advantage actor-critic (Adversarial A2C), which significantly improves the efficiency of dialogue policy learning in task-completio…
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning
Baolin Peng, Xiujun Li, Lihong Li +4
Building a dialogue agent to fulfill complex tasks, such as travel planning, is challenging because the agent has to learn to collectively complete multiple subtasks. For example,…