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20202023
most citedRegularizing Dialogue Generation by Imitating Implicit Scenarios

4 citations · 8 across the 11 of their papers we have counts for

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Showing 2021Show all

7 papers · 1 filter

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.IR2021★ 1 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.CL2021★ 1 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.…

cs.IR2021

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…