Learning a Simple and Effective Model for Multi-turn Response Generation with Auxiliary Tasks
Table 3: Human evaluation results. The ratios are calculated by combining annotations from three judges together.
['DailyDialog models', 'DailyDialog win (%)', 'DailyDialog loss (%)', 'DailyDialog tie (%)', 'DailyDialog kappa']
We can see that our model outperforms all baseline models, and most of the kappa values exceed 0.6 indicating substantial agreement among the annotators. Based on the annotation results, we find that our model tends to generate diverse and context consistent responses, indicating the effect of the auxiliary tasks.