A Syntactically Constrained Bidirectional-Asynchronous Approach for Emotional Conversation Generation
Table 2: The results of automatic evaluation (G–E = Greedy Matching, E–A = Embedding Average, V–E = Vector Extrema).
['Method', 'G–M', 'E–A', 'V–E', 'D–1', 'D–2']
Compared with the best model (S2S-AW) that considers only a single factor, E-SCBA makes significant improvement on the distinct metrics (+0.056 and +0.165), which verifies the effectiveness of taking both emotion and topic information into account to improve the diversity. Likewise, our approach also respectively achieves 0.042, 0.068 and 0.043 gains on G-M, E-A and V-E, benefiting from the compound information that captures the thrust of human conversation so that E-SCBA has a better ability to learn the goal distribution. Furthermore, the grades of the asynchronous models are higher than the synchronous models on both kinds of metrics, showing that the asynchronous method is a more suitable way for content-introducing conversation generation.