Unsupervised Injection of Knowledge into Dialogue Generationvia Language Models
Table 4: The perplexity of real paired knowledge on four datasets.
['[BOLD] data', '[BOLD] LM', '[BOLD] RealLM', '[BOLD] Decoupling']
The performance of knowledge generation models. To evaluate RealLM and Decoupling, we first feed the dialogue history (x), and then compute the Pσ(^z|x). The results indicates that the knowledge generation models trained by RealLM and Decoupling can catch the conditional information of knowledge given dialogue history, although they share parameters with the response generation parts.