AAAI Publications, Thirtieth AAAI Conference on Artificial Intelligence

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Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models
Iulian V. Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, Joelle Pineau

Last modified: 2016-03-05


We investigate the task of building open domain, conversational dialogue systems based on large dialogue corpora using generative models. Generative models produce system responses that are autonomously generated word-by-word, opening up the possibility for realistic, flexible interactions. In support of this goal, we extend the recently proposed hierarchical recurrent encoder-decoder neural network to the dialogue domain, and demonstrate that this model is competitive with state-of-the-art neural language models and back-off n-gram models. We investigate the limitations of this and similar approaches, and show how its performance can be improved by bootstrapping the learning from a larger question-answer pair corpus and from pretrained word embeddings.


Dialogue Systems, Cognitive Systems, Neural Networks, Generative Probabilistic Models, Word Embeddings, Transfer Learning

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