Global and Local Hierarchy-aware Contrastive Framework for Hierarchical Implicit Discourse Relation Recognition (ACL 2023)
arXiv preprint: https://arxiv.org/abs/2211.13873
-
Install
PyTorch
by following the instructions from the official website. -
Install
torch_geometric
by following the instructions from the official website. -
Run the following script to install the remaining dependencies,
pip install -r requirements.txt
- Download the PDTB 2.0 dataset, put it under /raw/
- Run the following script for data preprocessing,
python3 preprocess.py
(P.S. PDTB 3.0 can be downloaded from https://catalog.ldc.upenn.edu/LDC2019T05. You can easily modify preprocess.py and adapt it to PDTB 3.0.)
Run the following script for training, evaludating, and testing,
python3 run.py
(Our code can be easily run on a single NVIDIA GeForce RTX 3090)
If you find this work helpful, please cite our paper by:
@inproceedings{jiang-etal-2023-global,
title = "Global and Local Hierarchy-aware Contrastive Framework for Implicit Discourse Relation Recognition",
author = "Jiang, Yuxin and
Zhang, Linhan and
Wang, Wei",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.510",
pages = "8048--8064",
abstract = "Due to the absence of explicit connectives, implicit discourse relation recognition (IDRR) remains a challenging task in discourse analysis. The critical step for IDRR is to learn high-quality discourse relation representations between two arguments. Recent methods tend to integrate the whole hierarchical information of senses into discourse relation representations for multi-level sense recognition. Nevertheless, they insufficiently incorporate the static hierarchical structure containing all senses (defined as global hierarchy), and ignore the hierarchical sense label sequence corresponding to each instance (defined as local hierarchy). For the purpose of sufficiently exploiting global and local hierarchies of senses to learn better discourse relation representations, we propose a novel GlObal and Local Hierarchy-aware Contrastive Framework (GOLF), to model two kinds of hierarchies with the aid of multi-task learning and contrastive learning. Experimental results on PDTB 2.0 and PDTB 3.0 datasets demonstrate that our method remarkably outperforms current state-of-the-art models at all hierarchical levels.",
}