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        <title>Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models</title>
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    <name type="personal">
        <namePart type="given">Jianfeng</namePart>
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    <abstract>Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training. This paper addresses the problem by proposing a multi-task learning approach to training neural conversation models that leverages both conversation data across speakers and other types of data pertaining to the speaker and speaker roles to be modeled. Experiments show that our approach leads to significant improvements over baseline model quality, generating responses that capture more precisely speakers’ traits and speaking styles. The model offers the benefits of being algorithmically simple and easy to implement, and not relying on large quantities of data representing specific individual speakers.</abstract>
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%0 Conference Proceedings
%T Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models
%A Luan, Yi
%A Brockett, Chris
%A Dolan, Bill
%A Gao, Jianfeng
%A Galley, Michel
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F luan-etal-2017-multi
%X Building a persona-based conversation agent is challenging owing to the lack of large amounts of speaker-specific conversation data for model training. This paper addresses the problem by proposing a multi-task learning approach to training neural conversation models that leverages both conversation data across speakers and other types of data pertaining to the speaker and speaker roles to be modeled. Experiments show that our approach leads to significant improvements over baseline model quality, generating responses that capture more precisely speakers’ traits and speaking styles. The model offers the benefits of being algorithmically simple and easy to implement, and not relying on large quantities of data representing specific individual speakers.
%U https://aclanthology.org/I17-1061
%P 605-614

Markdown (Informal)

[Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models](https://aclanthology.org/I17-1061) (Luan et al., IJCNLP 2017)

• Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models (Luan et al., IJCNLP 2017)

ACL

• Yi Luan, Chris Brockett, Bill Dolan, Jianfeng Gao, and Michel Galley. 2017. Multi-Task Learning for Speaker-Role Adaptation in Neural Conversation Models. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 605–614, Taipei, Taiwan. Asian Federation of Natural Language Processing.