In a conversation or a dialogue process, attention and intention play intrinsic roles. This paper proposes a neural network based approach that models the attention and intention processes. It essentially consists of three recurrent networks. The encoder network is a word-level model representing source side sentences. The intention network is a recurrent network that models the dynamics of the intention process. The decoder network is a recurrent network produces responses to the input from the source side. It is a language model that is dependent on the intention and has an attention mechanism to attend to particular source side words, when predicting a symbol in the response. The model is trained end-to-end without labeling data. Experiments show that this model generates natural responses to user inputs.
@article{arxiv.1510.08565,
title = {Attention with Intention for a Neural Network Conversation Model},
author = {Kaisheng Yao and Geoffrey Zweig and Baolin Peng},
journal= {arXiv preprint arXiv:1510.08565},
year = {2015}
}