Papers › Pushing the Limits of AMR Parsing with Self-Learning

Pushing the Limits of AMR Parsing with Self-Learning

20 Oct 2020Findings of the Association for Computational Linguistics 2020arXiv:2010.10673archive 2025-07-28

Young-suk Lee, Ramon Fernandez Astudillo, Tahira Naseem, Revanth Gangi Reddy, Radu Florian, Salim Roukos

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.

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IBM/transition-amr-parser officialmentioned in papermentioned on GitHubpytorch report

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Tasks

AMR Parsing Abstract Meaning Representation Machine Translation Question Answering Self-Learning Transfer Learning Translation

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
AMR Parsing	LDC2014T12	stack-Transformer + self-learning (IBM)	F1 Full	78.2	#2 of 12	Archive leaderboard	report
AMR Parsing	LDC2017T10	stack-Transformer + self-learning (IBM)	Smatch	81.3	#16 of 27	Archive leaderboard	report

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