Neural Response Generation with Dynamic Vocabularies
Table 5: Performance in terms of content word number.
['[ITALIC] N', '0', '100', '1k', '3k', '5k', '10k']
Performance across different dynamic vocabulary sizes. Next, we examine how the performance of DVS2S changes with respect to the size of the target vocabularies. We vary the number of content words selected from the entire vocabulary according to {βi} in a range of {0,100,1000,3000,5000,10000}, and then check how the embedding based metrics change on the test data. The results are consistent with our intuition: we may lose important words for response generation when the number of selected words is too small (e.g., less than 100), but we cannot let the target vocabulary become too large either (e.g., larger than 1000) because that may involve many irrelevant words into generation. 1000 is the best choice as the performance of the model reaches its peak.