| 英文标题 | Open domain dialogue generation model based on multi-view adversarial learning |
| 作者英文名 | Zhang Liang, Yang Yan, Chen Chengcai, He Liang |
| 机构英文名 | 1.School of Computer Science & Technology,East China Normal University,Shanghai 200062,China;2.Shanghai Xiao'i Robot Technology Co. Ltd,Shanghai 201803,China |
| 英文摘要 | Recently, with the emergence and popularity of intelligent applications, non-task oriented dialogue system has played an increasingly important role in daily life. Generation-based dialogue systems receive extraordinary attention of some researchers because they are more flexible. In order to improve the fluency and contextual relevance of the responses generated by models, this paper proposed an open domain dialogue generation model based on binary discriminator in terms of a multi-view adversarial learning framework. The generator of the model rewrote a retrieved response to get a generated response. While the discriminator was composed of two binary classifiers and distinguished the human-generated responses from machine-generated ones. Experiments on a Chinese dialogue corpus show that the model has higher scores on both human and automatic evaluation than baselines. Experiments also show that multi-view training with binary discriminators can improve both the fluency and contextual relevance of the generated responses. |
| 英文关键词 | dialogue generation; dialogue system; adversarial learning; revise model |