Prototype-to-Style: Dialogue Generation with Style-Aware Editing on Retrieval Memory

• URL: http://arxiv.org/abs/2004.02214v1
• Date: Sun, 5 Apr 2020 14:36:15 GMT
• Title: Prototype-to-Style: Dialogue Generation with Style-Aware Editing on Retrieval Memory
• Authors: Yixuan Su, Yan Wang, Simon Baker, Deng Cai, Xiaojiang Liu, Anna Korhonen, Nigel Collier
• Abstract summary: We introduce a new prototype-to-style framework to tackle the challenge of stylistic dialogue generation. The framework uses an Information Retrieval (IR) system and extracts a response prototype from the retrieved response. A stylistic response generator then takes the prototype and the desired language style as model input to obtain a high-quality and stylistic response.
• Score: 65.98002918470543
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The ability of a dialog system to express prespecified language style during conversations has a direct, positive impact on its usability and on user satisfaction. We introduce a new prototype-to-style (PS) framework to tackle the challenge of stylistic dialogue generation. The framework uses an Information Retrieval (IR) system and extracts a response prototype from the retrieved response. A stylistic response generator then takes the prototype and the desired language style as model input to obtain a high-quality and stylistic response. To effectively train the proposed model, we propose a new style-aware learning objective as well as a de-noising learning strategy. Results on three benchmark datasets from two languages demonstrate that the proposed approach significantly outperforms existing baselines in both in-domain and cross-domain evaluations

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