Knowledge Aware Conversation Generation with Explainable Reasoning over Augmented Graphs
Table 4: Results of ablation study for AKGCM on EMNLP dataset. We also include results of the full model for comparison.
['[EMPTY]', 'BLEU-4', 'ROUGE-2', 'ROUGE-L']
AKGCM without (w/o) Non-factoid Knowledge: We see that without non-factoid knowledge from EMNLP dataset, the performance of our system drops significantly in terms of BLEU and ROUGE. It indicates that non-factoid knowledge is essential for knowledge aware conversation generation. AKGCM w/o the MRC Model or Bilinear One : For ablation study, we implement a few variants of our system without the bilinear model or MRC for knowledge selection. If we compare the performance of our full model with its variants, we find that both MRC and the bilinear model can bring performance improvement to our system. It indicates that the full interaction between messages and knowledge texts by their attention mechanism is effective to knowledge selection.