Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition
Yingxue Zhang, Fandong Meng, Peng Li, Ping Jian, Jie Zhou
Abstract
Implicit discourse relation recognition (IDRR) aims to identify logical relations between two adjacent sentences in the discourse. Existing models fail to fully utilize the contextual information which plays an important role in interpreting each local sentence. In this paper, we thus propose a novel graph-based Context Tracking Network (CT-Net) to model the discourse context for IDRR. The CT-Net firstly converts the discourse into the paragraph association graph (PAG), where each sentence tracks their closely related context from the intricate discourse through different types of edges. Then, the CT-Net extracts contextual representation from the PAG through a specially designed cross-grained updating mechanism, which can effectively integrate both sentence-level and token-level contextual semantics. Experiments on PDTB 2.0 show that the CT-Net gains better performance than models that roughly model the context.
BibTeX
@inproceedings{zhang-etal-2021-context,
title = "Context Tracking Network: Graph-based Context Modeling for Implicit Discourse Relation Recognition",
author = "Zhang, Yingxue and
Meng, Fandong and
Li, Peng and
Jian, Ping and
Zhou, Jie",
editor = "Toutanova, Kristina and
Rumshisky, Anna and
Zettlemoyer, Luke and
Hakkani-Tur, Dilek and
Beltagy, Iz and
Bethard, Steven and
Cotterell, Ryan and
Chakraborty, Tanmoy and
Zhou, Yichao",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.126/",
doi = "10.18653/v1/2021.naacl-main.126",
pages = "1592--1599"
}