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20182023
most citedAdaptive Parameterization for Neural Dialogue Generation

9 citations · 21 across the 9 of their papers we have counts for

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7 papers · 1 filter

cs.CL2021★ 5 cited

Constructing Emotion Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation

Lei Shen, Jinchao Zhang, Jiao Ou +2

Researches on dialogue empathy aim to endow an agent with the capacity of accurate understanding and proper responding for emotions. Existing models for empathetic dialogue generat…

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.CL2020★ 2 cited

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.…

cs.CL2020★ 3 cited

Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight

Hengyi Cai, Hongshen Chen, Yonghao Song +3

Current state-of-the-art neural dialogue models learn from human conversations following the data-driven paradigm. As such, a reliable training corpus is the crux of building a rob…

cs.CL2020

Learning from Easy to Complex: Adaptive Multi-curricula Learning for Neural Dialogue Generation

Hengyi Cai, Hongshen Chen, Cheng Zhang +5

Current state-of-the-art neural dialogue systems are mainly data-driven and are trained on human-generated responses. However, due to the subjectivity and open-ended nature of huma…

cs.CL2020★ 9 cited

Adaptive Parameterization for Neural Dialogue Generation

Hengyi Cai, Hongshen Chen, Cheng Zhang +3

Neural conversation systems generate responses based on the sequence-to-sequence (SEQ2SEQ) paradigm. Typically, the model is equipped with a single set of learned parameters to gen…

cs.CL2018★ 1 cited

KNPTC: Knowledge and Neural Machine Translation Powered Chinese Pinyin Typo Correction

Hengyi Cai, Xingguang Ji, Yonghao Song +4

Chinese pinyin input methods are very important for Chinese language processing. Actually, users may make typos inevitably when they input pinyin. Moreover, pinyin typo correction…