Name
Title | ||
|---|---|---|
CSAGN - Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling. |
Abstract | ||
|---|---|---|
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. |
Year | Venue | DocType |
|---|---|---|
2021 | EMNLP | Conference |
Volume | Citations | PageRank |
2021.emnlp-main | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Han Wu | 1 | 0 | 1.69 |
| Kun Xu | 2 | 0 | 0.34 |
| Linqi Song | 3 | 73 | 19.51 |