Posterior-GAN: Towards Informative and Coherent Response Generation with Posterior Generative Adversarial Network
Table 4: The results of Embedding-based Averaged Greedy Matching for response y with the given query x and future conversations z, which reflects the coherence of the response, and Frequency-based Similarity, which illustrates the informativeness of the response.
['Models', 'Averaged Greedy Matching for  [ITALIC] y and ( [ITALIC] x/ [ITALIC] z)', 'Frequency-based Similarity']
The informativeness of the responses is reflected by comparing the word frequency similarity between the generated response and the ground-truth response, where the responses are represented as a vector, and each element in the vector is denoted as the frequency of a word. Here, we use 2350 most frequent words from the training set of DailyDialog corpus without stop words and meaningless words. Regarding both the frequency similarity and matching degree, our model consistently outperforms the comparison models, which indicates that our model generates more informative and coherent responses.