9 citations · 9 across the 1 of their papers we have counts for
3 papers
cs.CL2020
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…