@inproceedings{lee-etal-2020-pushing,
title = "Pushing the Limits of {AMR} Parsing with Self-Learning",
author = "Lee, Young-Suk and
Fernandez Astudillo, Ram{\'o}n and
Naseem, Tahira and
Gangi Reddy, Revanth and
Florian, Radu and
Roukos, Salim",
editor = "Cohn, Trevor and
He, Yulan and
Liu, Yang",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.findings-emnlp.288",
doi = "10.18653/v1/2020.findings-emnlp.288",
pages = "3208--3214",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Pushing the Limits of AMR Parsing with Self-Learning
%A Lee, Young-Suk
%A Fernandez Astudillo, Ramón
%A Naseem, Tahira
%A Gangi Reddy, Revanth
%A Florian, Radu
%A Roukos, Salim
%Y Cohn, Trevor
%Y He, Yulan
%Y Liu, Yang
%S Findings of the Association for Computational Linguistics: EMNLP 2020
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F lee-etal-2020-pushing
%X 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.
%R 10.18653/v1/2020.findings-emnlp.288
%U https://aclanthology.org/2020.findings-emnlp.288
%U https://doi.org/10.18653/v1/2020.findings-emnlp.288
%P 3208-3214
Markdown (Informal)
[Pushing the Limits of AMR Parsing with Self-Learning](https://aclanthology.org/2020.findings-emnlp.288) (Lee et al., Findings 2020)
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.