Name
Title | ||
|---|---|---|
Learning Discourse-Level Diversity For Neural Dialog Models Using Conditional Variational Autoencoders |
Abstract | ||
|---|---|---|
While recent neural encoder-decoder models have shown great promise in modeling open-domain conversations, they often generate dull and generic responses. Unlike past work that has focused on diversifying the output of the decoder at word-level to alleviate this problem, we present a novel framework based on conditional variational autoencoders that captures the discourse-level diversity in the encoder. Our model uses latent variables to learn a distribution over potential conversational intents and generates diverse responses using only greedy decoders. We have further developed a novel variant that is integrated with linguistic prior knowledge for better performance. Finally, the training procedure is improved by introducing a bag-of-word loss. Our proposed models have been validated to generate significantly more diverse responses than baseline approaches and exhibit competence in discourse-level decision-making. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.18653/v1/P17-1061 | PROCEEDINGS OF THE 55TH ANNUAL MEETING OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS (ACL 2017), VOL 1 |
DocType | Volume | Citations |
Conference | abs/1703.10960 | 53 |
PageRank | References | Authors |
1.48 | 23 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| tiancheng zhao | 1 | 136 | 10.62 |
| Ran Zhao | 2 | 126 | 6.41 |
| Maxine Eskenazi | 3 | 979 | 127.53 |