output
20162024
most citedNeural Rating Regression with Abstractive Tips Generation for Recommendation

306 citations

Showing cs.CLShow all

9 papers · 1 filter

cs.CL20212 cited

DialogueBERT: A Self-Supervised Learning based Dialogue Pre-training Encoder

Zhenyu Zhang, Tao Guo, Meng Chen

With the rapid development of artificial intelligence, conversational bots have became prevalent in mainstream E-commerce platforms, which can provide convenient customer service t…

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.CL20213 cited

Progressive Multi-Granularity Training for Non-Autoregressive Translation

Liang Ding, Longyue Wang, Xuebo Liu +3

Non-autoregressive translation (NAT) significantly accelerates the inference process via predicting the entire target sequence. However, recent studies show that NAT is weak at lea…

cs.CL2021

Conversational Query Rewriting with Self-supervised Learning

Hang Liu, Meng Chen, Youzheng Wu +2

Context modeling plays a critical role in building multi-turn dialogue systems. Conversational Query Rewriting (CQR) aims to simplify the multi-turn dialogue modeling into a single…

cs.CL20202 cited

Modeling Topical Relevance for Multi-Turn Dialogue Generation

Hainan Zhang, Yanyan Lan, Liang Pang +3

Topic drift is a common phenomenon in multi-turn dialogue. Therefore, an ideal dialogue generation models should be able to capture the topic information of each context, detect th…

cs.CL20203 cited

Compose Like Humans: Jointly Improving the Coherence and Novelty for Modern Chinese Poetry Generation

Lei Shen, Xiaoyu Guo, Meng Chen

Chinese poetry is an important part of worldwide culture, and classical and modern sub-branches are quite different. The former is a unique genre and has strict constraints, while…