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arXiv · 2106.03065

Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues

Yinhe Zheng·Yida Wang·Pei Ke·Zhenyu Yang·Minlie Huang
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Abstract

This paper 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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Yinhe Zheng, Yida Wang, Pei Ke, Zhenyu Yang, Minlie Huang. 2021-06-06. Semantic-Enhanced Explainable Finetuning for Open-Domain Dialogues. https://arxiv.org/abs/2106.03065

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