Implicit discourse relation recognition as SRL-enhanced
[Objective]Implicit discourse relation recognition(IDRR)has traditionally been formalized as a classification task.In this study,we propose a novel approach that redefines IDRR as a text generation process,thus enabling direct generation of connective words between discourse units.This redefinition allows unambiguous mapping of connectives to discourse relations,aiming to improve precision in IDRR tasks.[Methods]Our approach treats IDRR as a sequence-to-sequence task.A connective-to-relation table is designed to map unambiguous connectives to specific discourse relations.Next,two substitution strategies are developed to replace ambiguous connectives in training instances.Furthermore,discourse units(DUs)are enriched with semantic role labels(SRL),providing the additional context.Finally,the model generates connectives based on these enhanced DUs.[Results]Experimental results on the English PDTB and Chinese CDTB datasets validate the effectiveness of this approach.The proposed method achieves state-of-the-art performance,significantly surpassing previous models in recognizing implicit discourse relations.Effectively,the connective generation resolves ambiguity and directly maps generated connectives to discourse relations.The substitution strategies and the connective-to-relation table enhance accuracy by ensuring unambiguous relational mapping.Overall,this approach demonstrates improved performance in capturing subtle semantic nuances within discourse.[Conclusion]Redefining IDRR as a generation task has shown substantial advantages in accurately capturing implicit discourse relations.By incorporating semantic role labeling and connective mapping strategies,this approach aligns closely with real-world discourse analysis needs.Hopefully,this proposed framework can provide a robust foundation for future advancements in discourse relation models and can be potentially applied in areas that require nuanced language comprehension.
万方数据
维普
