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2020.findings-emnlp.70.pdf
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🔗 Source: aclanthology.org
📊 Size: 1.18 MB
👤 Author: Hengyi Cai ; Hongshen Chen ; Yonghao Song ; zhuoye Ding ; Yongjun Bao ; Weipeng Yan ; Xiaofang Zhao
⬇️ Downloads: 711

Summary

Neural dialogue response generation has gained popularity, but models trained with Maximum Likelihood Estimation (MLE) objective function suffer from low-diversity issues. To address this, the authors introduce contrastive learning into dialogue generation, where the model perceives the difference between well-chosen positive and negative utterances. A pretrained baseline model is used as a reference, and the target dialogue model is trained to give higher conditional probabilities for positive samples and lower probabilities for negative samples. The authors also propose group-wise dual sampling to manage multi-mapping relations in human conversations, which encourages the model to pull matched sample pairs together and push mismatched pairs apart in the latent space. Experimental results show that the proposed framework is effective for training neural dialogue generation models with favorable performance over baseline training approaches.

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Description

Neural dialogue response generation has gained popularity, but models trained with Maximum Likelihood Estimation (MLE) objective function suffer from...

Technical Information

  • File Format: PDF
  • File Size: 1.18 MB
  • Pages: 10
  • Language: EN
  • Author: Hengyi Cai ; Hongshen Chen ; Yonghao Song ; zhuoye Ding ; Yongjun Bao ; Weipeng Yan ; Xiaofang Zhao
  • Total Downloads: 711
  • Last Updated: 3 weeks ago

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