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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…
Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network
Shaoxiong Feng, Hongshen Chen, Kan Li +1
Neural conversational models learn to generate responses by taking into account the dialog history. These models are typically optimized over the query-response pairs with a maximu…
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…