most citedRegularizing Dialogue Generation by Imitating Implicit Scenarios

4 citations · 6 across the 8 of their papers we have counts for

collaborators

10 papers

cs.CL2021

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…

cs.CL2021

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…

cs.CL2021

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…

cs.CL2021

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…

cs.IR20211 cited

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…

cs.CL20211 cited

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.…