  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.
