Unsupervised Discrete Sentence Representation Learning for Interpretable Neural Dialog Generation
Table 3: Homogeneity results (bounded [0, 1]).
['[EMPTY]', '[BOLD] SW Act', 'Topic', '[BOLD] DD Act', 'Emotion']
The next question is to interpret the meaning of the learned latent action symbols. To achieve this, the latent action of an utterance xn is obtained from a greedy mapping: an=argmaxkqR(z=k|xn). We set M=3 and K=5, so that there are at most 125 different latent actions, and each xn can now be represented by a1-a2-a3, e.g. “How are you?” → 1-4-2. Assuming that we have access to manually clustered data according to certain classes (e.g. dialog acts), it is unfair to use classic cluster measures Vinh et al. This is because the uniform prior p(z) evenly distribute the data to all possible latent actions, so that it is expected that frequent classes will be assigned to several latent actions. that measures if each latent action contains only members of a single class.