Improving Conversational Recommender Systems viaKnowledge Graph based Semantic Fusion
Table 4. Human evaluation results on the conversation task. Numbers marked with * indicate that the improvement is statistically significant compared with the best baseline (t-test with p-value <0.05).
['Models', 'Fluency', 'Informativeness']
However, we find that it tends to generate short and repetitive responses. This is the so-called “safe response” issue in the dialog generation task. Without additional supervision signal, it is likely to overfit to the frequent utterances in the training set. Second, KBRD performs best in terms of Informativeness score among the three baselines. It utilizes KG data to promote the probability of low-frequency words. Finally, our proposed model KGSF is consistently better than all the baselines with a large margin. We carefully design a KG-enhanced Transformer decoder. Our model is able to utilize contextual information effectively, and generate fluent and informative responses.