TDGCN:Research on Conversation Relationship Extraction of Two Stage Dynamic Graph Convolutional Networks Enhanced by Triggers
With the continuous increase of conversation data in the Internet,extracting relational triples from it is crucial for various downstream tasks of natural language processing.In order to improve the performance of dialogue relationship extraction,D.Yu et al.introduced the concept of"triggers"in the dataset,which provides important clues for relationship extraction.However,the current ap-plication of triggers is only limited to using them as an additional task for model training,and has not been fully utilized in relational triplet reasoning.This article proposes a two-stage dynamic graph model,which effectively improves the ambiguity problem of existing statically constructed graph attention models when dealing with overlapping relationships by introducing dynamic mechanisms.And trigger nodes are introduced in the dynamic graph model to more fully utilize triggers for relational reasoning.The entire model was tested on the DialogRE dataset,and compared to the baseline model,the F1 value of the model increased by 2.2％on the validation set and 2％on the test set.And this article further analyzes the proposed mechanism and verifies its effectiveness through experiments.
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