Building Endtoend Dialogue Systems Using Generative Hierarchical Neural Network Models

# Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models

**arXiv ID:** 1507.04808
**Authors:** Iulian V. Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, Joelle Pineau
**Published:** 2015-07-17T00:21:39Z
**Abstract:**
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.

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## References

- [arXiv:1507.04808](http://arxiv.org/abs/1507.04808v3)