| Title: | OS1-4 Comparison Between Variational Autoencoder and Encoder-Decoder Models for Short Conversation |
|---|---|
| Publication: | ICAROB2017 |
| Volume: | 22 |
| Pages: | 639-642 |
| ISSN: | 2188-7829 |
| DOI: | 10.5954/ICAROB.2017.OS1-4 |
| Author(s): | Shin Asakawa, Takashi Ogata |
| Publication Date: | January 19, 2017 |
| Keywords: | neural network language model, recurrent neural networks, vector embedding, variational autoencoder, sequential data processing, variational inference, narrative generation |
| Abstract: | We provide a point of view concerning generative models such that they could deal with short conversation. These include the standard recurrent neural network language, sequence to sequence, vector embedding, and variational autoencoder models. These models seem to be possible candidates to describe such conversations, there are several differences among them. |
| PDF File: | https://alife-robotics.co.jp/members2017/icarob/data/html/data/OS_pdf/OS1/OS1-4.pdf |
| Copyright: | © The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/ |
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