| Paper ID | C-2-3.2 |
| Paper Title |
Efficient Diverse Response Generation in Attention-based Neural Conversational Model with Maximum Mutual Information |
| Authors |
Yuki Kishida, Tsuneo Kato, Doshisha University, Japan; Yanan Wang, Jianming Wu, Gen Hattori, KDDI Research, Inc., China |
| Session |
C-2-3: Machine Learning and Data Analysis 1 |
| Time | Wednesday, 09 December, 17:15 - 19:15 |
| Presentation Time: | Wednesday, 09 December, 17:30 - 17:45 Check your Time Zone |
|
All times are in New Zealand Time (UTC +13) |
| Topic |
Machine Learning and Data Analytics (MLDA): |
| Abstract |
Diversity of generated responses is important for a data-driven neural conversational model (NCM) for non-task-oriented conversation. A criterion of maximum mutual information (MMI) and generating N-best outputs are both effective ways to increase the diversity. Generally, a beam search is used for generating N-best outputs. However, the beam search is likely to produce similar outputs in the N-best results. We propose a simple and efficient N-best search, namely N-greedy search, for an encoder-decoder recurrent neural network (RNN) with an attention mechanism. We built an NCM with a fictive chitchat corpus and generated responses based on the MMI criterion and N-greedy search. All of four objective indices of diversity showed increases, and a subjective evaluation clearly showed a reduction in the number of dull responses. |