Retrieval-Enhanced Adversarial Training for Neural Response Generation
Table 5: Automatic evaluation results of the number of distinct uni-grams (# of UNI) and bi-grams (# of BI), Dist-1, Dist-2 and Originality (Origin). D+ and G+ are two variants of our approach where candidates are only available for the discriminator and the generator, respectively.
['Model', '# of UNI', 'Dist-1', '# of BI', 'Dist-2', 'Origin']
AL outperforms S2S in all metrics, indicating that adversarial training is helpful for generating diverse n-grams and responses. By introducing N-best response candidates, our approach further increases Dist-2 by 0.112 based on AL (from 0.124 to 0.236) and the improvement is significant (t-test, p<0.01). In contrast, the increase of Dist-2 after combining N-best response candidates in MLE based approach is only 0.062, comparing MS2S with S2S. This suggests that introducing a discriminator with adversarial training is more effective than MLE objective in utilizing N-best response candidates to generate more diverse n-grams. Note that the improvement after introducing candidates in Dist-1 and Originality is not as significant as that in Dist-2. This is because responses of MLE based models (MS2S and EDIT) tend to contain informative content with simple sentence structures, like “… is (not) good.” To understand the importance of different components of our approach, we also train two variants: D+ and G+, where N-best response candidates are only available for the discriminator and the generator, respectively. Note that AL does not utilize candidates in the generator nor the discriminator, thus can be seen as a start point of D+ and G+. The improvement in G+ is more significant as its generator can directly utilize the candidates as generation materials. While candidates’ information in D+ is compressed into a discriminative signal by the discriminator. Nevertheless, introducing candidates into the discriminator helps to generate more diverse responses comparing AL with D+, and G+ with Ours, demonstrating that the retrieval-enhanced discriminator is able to benefit the generator.