LSTM based Conversation Models

2016-03-31 · Yi Luan, Yangfeng Ji, Mari Ostendorf

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.

📄 PDF Abstract BibTeX arXiv:1603.09457

Code (1)

michaelfarrell76/End-To-End-Generative-Dialogue torch

Tasks

Language ModelingLanguage Modelling Text Generation

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