Adaptive Parameterization for Neural Dialogue Generation
Table 3: Speed test.
['[BOLD] Models', '[BOLD] Speed (cases/ms)']
Augmented with auxiliary components, all the extension models exhibit higher time cost than the original SEQ2SEQ model. We observe that the decoding speeds of CVAE and LAED are relatively comparable with our model. However, when comparing with TA-SEQ2SEQ and DOM-SEQ2SEQ that also elaborately and explicitly model conversations with diverse topics or themes, AdaND shows a clear superiority in decoding speed. For TA-SEQ2SEQ, it relies on an outside LDA model to obtain the topic information. The joint attention and copying mechanism also reduce its efficiency. For DOM-SEQ2SEQ, it is not surprising that the time complexity of multiple topic/theme-specific encoder-decoders is much higher than all-other comparison models. AdaND utilizes a single encoder-decoder and is parameterized dynamically regarding the input context, which ensures its flexibility and efficiency.