Neural Response Generation with Dynamic Vocabularies
Table 1: Automatic evaluation results. Numbers in bold mean that improvement from the model on that metric is statistically significant over the baseline methods (t-test, p-value <0.01).
['[EMPTY]', 'BLEU-1', 'BLEU-2', 'BLEU-3', 'Average', 'Extrema', 'Greedy', 'Distinct-1', 'Distinct-2']
DVS2S and S-DVS2S significantly outperform the baseline methods on most metrics, demonstrating the effectiveness of the dynamic vocabulary mechanism on response generation for open domain dialogues. Moreover, DVS2S also significantly improves upon S-DVS2S on metrics except BLEU-2 and BLEU-3. The results verify the advantage of joint learning of vocabulary and generation. DVS2S is significantly better than all baseline methods on distinct-1 and distinct-2, indicating that the model can generate more diverse and informative responses. This is because with the dynamic vocabulary mechanism, the model can circumvent the influence from generic patterns when frequent but irrelevant nouns, verbs, adjectives, and adverbs are excluded from decoding, and pay more attention to useful content words in decoding.