6 citations · 9 across the 6 of their papers we have counts for
6 papers
FORCE: A Framework of Rule-Based Conversational Recommender System
Jun Quan, Ze Wei, Qiang Gan +11
The conversational recommender systems (CRSs) have received extensive attention in recent years. However, most of the existing works focus on various deep learning models, which ar…
Integrating Pre-trained Model into Rule-based Dialogue Management
Jun Quan, Meng Yang, Qiang Gan +9
Rule-based dialogue management is still the most popular solution for industrial task-oriented dialogue systems for their interpretablility. However, it is hard for developers to m…
RiSAWOZ: A Large-Scale Multi-Domain Wizard-of-Oz Dataset with Rich Semantic Annotations for Task-Oriented Dialogue Modeling
Jun Quan, Shian Zhang, Qian Cao +2
In order to alleviate the shortage of multi-domain data and to capture discourse phenomena for task-oriented dialogue modeling, we propose RiSAWOZ, a large-scale multi-domain Chine…
Modeling Long Context for Task-Oriented Dialogue State Generation
Jun Quan, Deyi Xiong
Based on the recently proposed transferable dialogue state generator (TRADE) that predicts dialogue states from utterance-concatenated dialogue context, we propose a multi-task lea…
Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue
Jun Quan, Deyi Xiong
The training of task-oriented dialogue systems is often confronted with the lack of annotated data. In contrast to previous work which augments training data through expensive crow…
GECOR: An End-to-End Generative Ellipsis and Co-reference Resolution Model for Task-Oriented Dialogue
Jun Quan, Deyi Xiong, Bonnie Webber +1
Ellipsis and co-reference are common and ubiquitous especially in multi-turn dialogues. In this paper, we treat the resolution of ellipsis and co-reference in dialogue as a problem…