LSTM based Conversation Models

Yi Luan; Yangfeng Ji; Mari Ostendorf

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arXiv · 1603.09457

LSTM based Conversation Models

Yi Luan·Yangfeng Ji·Mari Ostendorf

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Abstract

In this paper, we present a conversational model that incorporates both context and participant role for two-party conversations. Different architectures are explored for integrating participant role and context information into a Long Short-term Memory (LSTM) language model. The conversational model can function as a language model or a language generation model. Experiments on the Ubuntu Dialog Corpus show that our model can capture multiple turn interaction between participants. The proposed method outperforms a traditional LSTM model as measured by language model perplexity and response ranking. Generated responses show characteristic differences between the two participant roles.

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BibTeX RIS

Yi Luan, Yangfeng Ji, Mari Ostendorf. 2016-03-31. LSTM based Conversation Models. https://arxiv.org/abs/1603.09457

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