Polite Dialogue Generation Without Parallel Data
Table 6: Analysis of politeness classifier: sampled responses from MovieTriples dialogue corpus (without cherry-picking) and their politeness score. The double line separates polite and rude classified responses.
['[BOLD] Target Sequence', '[BOLD] Score']
We start our analysis by providing qualitative examples of how well our politeness classifier performs on the target sequences from MovieTriples train dataset. This is important to check because the classifier is trained on Wikipedia ( Wiki) admin request messages, and while our LSTM-CNN performs better on cross-domain StackExchange (SE) data, the MovieTriples dialogue corpus is still quite different and diverse in domain from both Wiki and SE. Hence, it is important to have a reasonably accurate politeness classifier such that it can provide useful labels and rewards for our polite-dialogue models. We can see that the classifier provides a reasonably correct score a majority of the time, capturing several psycholinguistic politeness strategies mentioned in \newciteDanescu-niculescu-mizil2013, e.g., positive ones such as gratitude, deference, greeting, positive lexicon, indirection, indicative modal, and negative ones such as negative lexicon, direct question, direct start, 2nd person start. However, it does occasionally give strongly polite or rude scores to some mild or neutral responses, e.g., ”they were in a car accident”, showing scope for classifier improvements.