%0 Generic %1 yao2015attention %A Yao, Kaisheng %A Zweig, Geoffrey %A Peng, Baolin %D 2015 %K attention-rnn dialogue nn rnn %T Attention with Intention for a Neural Network Conversation Model %U http://arxiv.org/abs/1510.08565 %X 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.

@misc{yao2015attention, abstract = {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.}, added-at = {2016-12-03T03:37:16.000+0100}, author = {Yao, Kaisheng and Zweig, Geoffrey and Peng, Baolin}, biburl = {https://www.bibsonomy.org/bibtex/228c64559d10071b10a18a18be277e6f5/jkan}, description = {Attention with Intention for a Neural Network Conversation Model}, interhash = {3e11a159a2b0bbbdf8829d26bdff428b}, intrahash = {28c64559d10071b10a18a18be277e6f5}, keywords = {attention-rnn dialogue nn rnn}, note = {cite arxiv:1510.08565}, timestamp = {2016-12-03T03:37:16.000+0100}, title = {Attention with Intention for a Neural Network Conversation Model}, url = {http://arxiv.org/abs/1510.08565}, year = 2015 }