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

@inproceedings{Serban2016BuildingED,
  title={Building End-To-End Dialogue Systems Using Generative Hierarchical Neural Network Models},
  author={Iulian Serban and Alessandro Sordoni and Yoshua Bengio and Aaron C. Courville and Joelle Pineau},
  booktitle={AAAI},
  year={2016}
}
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… CONTINUE READING
Highly Influential
This paper has highly influenced 55 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 503 citations. REVIEW CITATIONS
Recent Discussions
This paper has been referenced on Twitter 26 times over the past 90 days. VIEW TWEETS

Citations

Publications citing this paper.
Showing 1-10 of 349 extracted citations

504 Citations

0100200300201620172018
Citations per Year
Semantic Scholar estimates that this publication has 504 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 42 references

Similar Papers

Loading similar papers…