2016 BuildingEndtoEndDialogueSystems

• (Serban et al., 2016) ⇒ Iulian V. Serban, Alessandro Sordoni, Yoshua Bengio, Aaron Courville, and Joelle Pineau. (2016). “Building End-to-end Dialogue Systems Using Generative Hierarchical Neural Network Models.” In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence.

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222016%22+Building+End-to-end+Dialogue+Systems+Using+Generative+Hierarchical+Neural+Network+Models
• http://dl.acm.org/citation.cfm?id=3016387.3016435&preflayout=flat#citedby

Quotes

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 backoff 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.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 BuildingEndtoEndDialogueSystems	Yoshua Bengio Aaron Courville Iulian V. Serban Alessandro Sordoni Joelle Pineau			Building End-to-end Dialogue Systems Using Generative Hierarchical Neural Network Models						2016