4 citations · 8 across the 11 of their papers we have counts for
11 papers
Vague Preference Policy Learning for Conversational Recommendation
Gangyi Zhang, Chongming Gao, Wenqiang Lei +6
Conversational recommendation systems (CRS) commonly assume users have clear preferences, leading to potential over-filtering of relevant alternatives. However, users often exhibit…
Adaptive Bridge between Training and Inference for Dialogue
Haoran Xu, Hainan Zhang, Yanyan Zou +3
Although exposure bias has been widely studied in some NLP tasks, it faces its unique challenges in dialogue response generation, the representative one-to-various generation scena…
FCM: A Fine-grained Comparison Model for Multi-turn Dialogue Reasoning
Xu Wang, Hainan Zhang, Shuai Zhao +5
Despite the success of neural dialogue systems in achieving high performance on the leader-board, they cannot meet users' requirements in practice, due to their poor reasoning skil…
Identifying Untrustworthy Samples: Data Filtering for Open-domain Dialogues with Bayesian Optimization
Lei Shen, Haolan Zhan, Xin Shen +3
Being able to reply with a related, fluent, and informative response is an indispensable requirement for building high-quality conversational agents. In order to generate better re…
Topic-Aware Contrastive Learning for Abstractive Dialogue Summarization
Junpeng Liu, Yanyan Zou, Hainan Zhang +4
Unlike well-structured text, such as news reports and encyclopedia articles, dialogue content often comes from two or more interlocutors, exchanging information with each other. In…
Improving Sequential Recommendation Consistency with Self-Supervised Imitation
Xu Yuan, Hongshen Chen, Yonghao Song +4
Most sequential recommendation models capture the features of consecutive items in a user-item interaction history. Though effective, their representation expressiveness is still h…
Probing Product Description Generation via Posterior Distillation
Haolan Zhan, Hainan Zhang, Hongshen Chen +5
In product description generation (PDG), the user-cared aspect is critical for the recommendation system, which can not only improve user's experiences but also obtain more clicks.…
User-Inspired Posterior Network for Recommendation Reason Generation
Haolan Zhan, Hainan Zhang, Hongshen Chen +4
Recommendation reason generation, aiming at showing the selling points of products for customers, plays a vital role in attracting customers' attention as well as improving user ex…
Group-wise Contrastive Learning for Neural Dialogue Generation
Hengyi Cai, Hongshen Chen, Yonghao Song +4
Neural dialogue response generation has gained much popularity in recent years. Maximum Likelihood Estimation (MLE) objective is widely adopted in existing dialogue model learning.…
Regularizing Dialogue Generation by Imitating Implicit Scenarios
Shaoxiong Feng, Xuancheng Ren, Hongshen Chen +3
Human dialogues are scenario-based and appropriate responses generally relate to the latent context knowledge entailed by the specific scenario. To enable responses that are more m…
Collaborative Group Learning
Shaoxiong Feng, Hongshen Chen, Xuancheng Ren +3
Collaborative learning has successfully applied knowledge transfer to guide a pool of small student networks towards robust local minima. However, previous approaches typically str…