Adaptive Parameterization for Neural Dialogue Generation
Table 1: Quantitative evaluation results (%).
['[BOLD] Models', '[BOLD] Relevance\xa0(%)  [BOLD] BLEU', '[BOLD] Relevance\xa0(%)  [BOLD] Average', '[BOLD] Relevance\xa0(%)  [BOLD] Greedy', '[BOLD] Relevance\xa0(%)  [BOLD] Extrema', '[BOLD] Informativeness\xa0(%)  [BOLD] Distinct-1', '[BOLD] Informativeness\xa0(%)  [BOLD] Distinct-2', '[BOLD] Informativeness\xa0(%)  [BOLD] Distinct-3']
CVAE and LAED inject SEQ2SEQ with stochastic latent variable, resulting in more informative responses and better performance on Distinct-{1, 2, 3}. TA-SEQ2SEQ incorporates SEQ2SEQ with the outsourcing topic information from LDA. It is not surprising that it performs much better on the response relevance (BLEU, Average, Greedy, Extrema), while its improvements on the informativeness are limited. DOM-SEQ2SEQ builds multiple domain-specific encoder-decoders. It gains improvements on both the relevance metrics and informativeness metrics. Context-aware parameterization captures local semantics of the given context, while topic-aware parameterization enables parameters sharing among conversations with similar topics. When jointly utilizing both the context-aware and topic-aware parameterization mechanisms, we observe the best performance, indicating that these two mechanisms are both beneficial and complementary.