Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues

• URL: http://arxiv.org/abs/2106.03065v1
• Date: Sun, 6 Jun 2021 09:03:41 GMT
• Title: Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues
• Authors: Chen Henry Wu, Yinhe Zheng, Yida Wang, Zhenyu Yang, Minlie Huang
• Abstract summary: We propose to combine pretrained language models with the modular dialogue paradigm for open-domain dialogue modeling. Our method, semantic-enhanced finetuning, instantiates conversation understanding, planning, and response generation as a language model finetuning task.
• Score: 33.50099424582726
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we propose to combine pretrained language models with the modular dialogue paradigm for open-domain dialogue modeling. Our method, semantic-enhanced finetuning, instantiates conversation understanding, planning, and response generation as a language model finetuning task. At inference, we disentangle semantic and token variations by specifying sampling methods and constraints for each module separately. For training and evaluation, we present X-Weibo, a Chinese multi-turn open-domain dialogue dataset with automatic annotation for emotions, DAs, and topical words. Experiments show that semantic-enhanced finetuning outperforms strong baselines on non-semantic and semantic metrics, improves the human-evaluated relevance, coherence, and informativeness, and exhibits considerable controllability over semantic variables.

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