Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs
Table 2: Results of automatic evaluations on the two datasets.
['[EMPTY]', 'EMNLP dialog dataset BLEU-4', 'EMNLP dialog dataset ROUGE-2', 'EMNLP dialog dataset ROUGE-L', 'EMNLP dialog dataset Hit@1', 'ICLR dialog dataset BLEU-4', 'ICLR dialog dataset ROUGE-2', 'ICLR dialog dataset ROUGE-L', 'ICLR dialog dataset Hit@1']
Results: It indicates that AKGCM has a capability of knowledge selection better than BiDAF and TMemNet, and generates more informative and grammatical responses. We notice that from EMNLP dataset to ICLR dataset, there is a significant performance drop for almost all the models. It is probably due to that the quality of ICLR dataset is worse than that of EMNLP dataset. A common phenomenon of ICLR dataset is that the knowledge used in responses is loosely relevant to input messages, which increases the difficulty of model learning.