9 citations · 21 across the 9 of their papers we have counts for
9 papers
Cloudless-Training: A Framework to Improve Efficiency of Geo-Distributed ML Training
Wenting Tan, Xiao Shi1, Cunchi Lv +1
Geo-distributed ML training can benefit many emerging ML scenarios (e.g., large model training, federated learning) with multi-regional cloud resources and wide area network. Howev…
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…
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…
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…
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.…
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…
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…
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…
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…