Examining Dialogue Consistency Based on Chapter-Level Semantic Graph
[Objective]This paper integrates chapter-level semantic graphs to improve the accuracy of dialogue consistency detection.[Methods]First,we used the pre-trained language model BERT to encode the dialogue context and knowledge base.Then,we constructed a dialogue chapter-level semantic graph containing coreference chains and abstract meaning representations.Third,we captured the semantic information of the constructed graph using a multi-relation graph convolutional network.Finally,we built multiple classifiers to predict dialogue inconsistency.[Results]We examined our new model on the CI-ToD benchmark dataset and compared its performance with the existing models.The proposed model's F1 value improved by more than 1％over the optimal models.[Limitations]The proposed model cannot address the co-referential entity omission in dialogues.[Conclusions]Integrating various types of semantic information,such as coreference chains and abstract meaning representations,can effectively improve the performance of dialogue consistency detection.
Dialogue SystemConsistency DetectionCoreference ChainAbstract Meaning RepresentationGraph Convolutional Network
NETL
NSTL
万方数据
