| Challenge: | Existing methods to adapt dialog data to new domains with limited resources are expensive . a domain adaptive dialog system model learns from multiple rich-resource tasks and adapts to new tasks with minimal training samples. |
| Approach: | They propose a domain adaptive dialog generation method based on meta-learning . they train a dialog system model using multiple rich-resource single-domain dialog data . |
| Outcome: | The proposed method can learn a competitive dialog system on a new domain with minimal training examples. |
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| Challenge: | Sentence function is an important linguistic feature indicating the communicative purpose of a sentence in a conversation. |
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| Challenge: | Existing methods to train a multi-domain dialogue state tracker are lacking in accuracy. |
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Yizhe Zhang, Siqi Sun, Michel Galley, Yen-Chun Chen, Chris Brockett, Xiang Gao, Jianfeng Gao, Jingjing Liu, Bill Dolan
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Human-centric dialog training via offline reinforcement learning (2020.emnlp-main)
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Natasha Jaques, Judy Hanwen Shen, Asma Ghandeharioun, Craig Ferguson, Agata Lapedriza, Noah Jones, Shixiang Gu, Rosalind Picard
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Low Resource Style Transfer via Domain Adaptive Meta Learning (2022.naacl-main)
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