CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling. (arXiv:2109.11541v1 [cs.CL])

Conversational semantic role labeling (CSRL) is believed to be a crucial step
towards dialogue understanding. However, it remains a major challenge for
existing CSRL parser to handle conversational structural information. In this
paper, we present a simple and effective architecture for CSRL which aims to
address this problem. Our model is based on a conversational structure-aware
graph network which explicitly encodes the speaker dependent information. We
also propose a multi-task learning method to further improve the model.
Experimental results on benchmark datasets show that our model with our
proposed training objectives significantly outperforms previous baselines.

Source: https://arxiv.org/abs/2109.11541


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