mlsanityData to 7 Oct 2026

CSAGN: Conversational Structure Aware Graph Network for Conversational Semantic Role Labeling

arXiv 2109.11541 ↗First posted 23 Sep 2021cs.CL cs.AI cs.LGcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 14 of its 15 references that are in the map. First posted on arXiv 23 Sep 2021.

Builds on

14 of 15 references.

References · 14

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  9. 2019DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversationcited by 184
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2 more
  1. 2021NaturalConv: A Chinese Dialogue Dataset Towards Multi-turn Topic-driven Conversationcited by 35
  2. 2021Conversational Semantic Role Labelingcited by 8

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Cited by year

Citing papers per year: 2022: 4; 2023: 1.

5 papers.

  1. 2022DialoKG: Knowledge-Structure Aware Task-Oriented Dialogue Generationcited by 19
  2. 2022Conversational Semantic Role Labeling with Predicate-Oriented Latent Graphcited by 14
  3. 2022Learning Locality and Isotropy in Dialogue Modelingcited by 1
  4. 2022Zero-shot Cross-lingual Conversational Semantic Role Labelingcited by 0
  5. 2023Friend-training: Learning from Models of Different but Related Taskscited by 0