Generating More Interesting Responses in Neural Conversation Models with Distributional Constraints
Table 6: Comparing the model variation by reducing beam size to 10 and also comparing decoder constraints without MMI reranking
['Model Alias', 'No (%)', 'Unsure (%)', 'Yes (%)']
To see the effectiveness of our decoding constraints separately, we compare the best performing DC-MMI200 model with DC-10 and DC-MMI10, both of which use a beam size of 10 – DC-10 does not include MMI reranking. We observe that with a beam size of 10 our model is able to generate content rich responses, but suffers in terms of plausibility. The values in the table suggests the decoding constraints defined in this work successfully inject content words into candidate hypotheses and that MMI is able to effectively choose plausible candidates. In the case of DC-10 and DC-MMI10, both models generate the same candidates, but MMI is able to re-rank the results and thus improves plausibility.