Abstract
Pre-trained language models (PLMs) have shown great potentials in natural language processing (NLP) including rhetorical structure theory (RST) discourse parsing. Current PLMs are obtained by sentence-level pre-training, which is different from the basic processing unit, i.e. element discourse unit (EDU).To this end, we propose a second-stage EDU-level pre-training approach in this work, which presents two novel tasks to learn effective EDU representations continually based on well pre-trained language models. Concretely, the two tasks are (1) next EDU prediction (NEP) and (2) discourse marker prediction (DMP).We take a state-of-the-art transition-based neural parser as baseline, and adopt it with a light bi-gram EDU modification to effectively explore the EDU-level pre-trained EDU representation. Experimental results on a benckmark dataset show that our method is highly effective,leading a 2.1-point improvement in F1-score. All codes and pre-trained models will be released publicly to facilitate future studies.- Anthology ID:
- 2022.acl-long.294
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4269–4280
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.294
- DOI:
- 10.18653/v1/2022.acl-long.294
- Cite (ACL):
- Nan Yu, Meishan Zhang, Guohong Fu, and Min Zhang. 2022. RST Discourse Parsing with Second-Stage EDU-Level Pre-training. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4269–4280, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- RST Discourse Parsing with Second-Stage EDU-Level Pre-training (Yu et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2022.acl-long.294.pdf
- Code
- yunan4nlp/e-nnrstparser