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.
@article{arxiv.1603.09457,
title = {LSTM based Conversation Models},
author = {Yi Luan and Yangfeng Ji and Mari Ostendorf},
journal= {arXiv preprint arXiv:1603.09457},
year = {2016}
}