Pushing the Limits of AMR Parsing with Self-Learning

Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Revanth Gangi Reddy, Radu Florian, Salim Roukos


Abstract
Abstract Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR. At the same time, self-learning techniques have helped push the performance boundaries of other natural language processing applications, such as machine translation or question answering. In this paper, we explore different ways in which trained models can be applied to improve AMR parsing performance, including generation of synthetic text and AMR annotations as well as refinement of actions oracle. We show that, without any additional human annotations, these techniques improve an already performant parser and achieve state-of-the-art results on AMR 1.0 and AMR 2.0.
Anthology ID:
2020.findings-emnlp.288
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2020
Month:
November
Year:
2020
Address:
Online
Venues:
EMNLP | Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3208–3214
Language:
URL:
https://aclanthology.org/2020.findings-emnlp.288
DOI:
10.18653/v1/2020.findings-emnlp.288
Bibkey:
Cite (ACL):
Young-Suk Lee, Ramón Fernandez Astudillo, Tahira Naseem, Revanth Gangi Reddy, Radu Florian, and Salim Roukos. 2020. Pushing the Limits of AMR Parsing with Self-Learning. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3208–3214, Online. Association for Computational Linguistics.
Cite (Informal):
Pushing the Limits of AMR Parsing with Self-Learning (Lee et al., Findings 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.findings-emnlp.288.pdf
Data
LDC2017T10