Neural Response Generation with Dynamic Vocabularies
Table 2: Human evaluation results. The ratios are calculated by combining the annotations from the three judges together.
['[EMPTY]', '0', '1', '2', 'Kappa']
DVS2S generates much more informative and interesting responses (2 responses) and much less invalid responses (0 responses) than the baseline methods. The results are consistent with the automatic evaluation results. S-DVS2S is much worse than DVS2S on 0 responses. This is because the gap between training and test in S-DVS2S leads to more grammatical broken and irrelevant responses. We also conduct a t-test between DVS2S and the baseline models and results show that the improvement from our model is statistically significant (p-value <0.01).