AMR-DA: Data Augmentation by Abstract Meaning Representation

Ziyi Shou; Yuxin Jiang; Fangzhen Lin

doi:10.18653/v1/2022.findings-acl.244

AMR-DA: Data Augmentation by Abstract Meaning Representation

Ziyi Shou, Yuxin Jiang, Fangzhen Lin

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

29 Citations (Scopus)

Abstract

Meaning Representation (AMR) is a semantic representation for NLP/NLU. In this paper, we propose to use it for data augmentation in NLP. Our proposed data augmentation technique, called AMR-DA, converts a sample sentence to an AMR graph, modifies the graph according to various data augmentation policies, and then generates augmentations from graphs. Our method combines both sentence-level techniques like back translation and token-level techniques like EDA (Easy Data Augmentation). To evaluate the effectiveness of our method, we apply it to the English tasks of semantic textual similarity (STS) and text classification. For STS, our experiments show that AMR-DA boosts the performance of the state-of-the-art models on several STS benchmarks. For text classification, AMR-DA outperforms EDA and AEDA and leads to more robust improvements.

Original language	English
Title of host publication	ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Findings of ACL 2022
Editors	Smaranda Muresan, Preslav Nakov, Aline Villavicencio
Publisher	Association for Computational Linguistics (ACL)
Pages	3082-3098
Number of pages	17
ISBN (Electronic)	9781955917254
DOIs	https://doi.org/10.18653/v1/2022.findings-acl.244
Publication status	Published - 2022
Externally published	Yes
Event	Findings of the Association for Computational Linguistics: ACL 2022 - Dublin, Ireland Duration: 22 May 2022 → 27 May 2022

Publication series

Name	Proceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)	0736-587X

Conference

Conference	Findings of the Association for Computational Linguistics: ACL 2022
Country/Territory	Ireland
City	Dublin
Period	22/05/22 → 27/05/22

Bibliographical note

Publisher Copyright:
© 2022 Association for Computational Linguistics.

Access to Document

10.18653/v1/2022.findings-acl.244

Cite this

Shou, Z., Jiang, Y., & Lin, F. (2022). AMR-DA: Data Augmentation by Abstract Meaning Representation. In S. Muresan, P. Nakov, & A. Villavicencio (Eds.), ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Findings of ACL 2022 (pp. 3082-3098). (Proceedings of the Annual Meeting of the Association for Computational Linguistics). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.findings-acl.244

Shou, Ziyi ; Jiang, Yuxin ; Lin, Fangzhen. / AMR-DA : Data Augmentation by Abstract Meaning Representation. ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Findings of ACL 2022. editor / Smaranda Muresan ; Preslav Nakov ; Aline Villavicencio. Association for Computational Linguistics (ACL), 2022. pp. 3082-3098 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

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title = "AMR-DA: Data Augmentation by Abstract Meaning Representation",

abstract = "Meaning Representation (AMR) is a semantic representation for NLP/NLU. In this paper, we propose to use it for data augmentation in NLP. Our proposed data augmentation technique, called AMR-DA, converts a sample sentence to an AMR graph, modifies the graph according to various data augmentation policies, and then generates augmentations from graphs. Our method combines both sentence-level techniques like back translation and token-level techniques like EDA (Easy Data Augmentation). To evaluate the effectiveness of our method, we apply it to the English tasks of semantic textual similarity (STS) and text classification. For STS, our experiments show that AMR-DA boosts the performance of the state-of-the-art models on several STS benchmarks. For text classification, AMR-DA outperforms EDA and AEDA and leads to more robust improvements.",

author = "Ziyi Shou and Yuxin Jiang and Fangzhen Lin",

note = "Publisher Copyright: {\textcopyright} 2022 Association for Computational Linguistics.; Findings of the Association for Computational Linguistics: ACL 2022 ; Conference date: 22-05-2022 Through 27-05-2022",

year = "2022",

doi = "10.18653/v1/2022.findings-acl.244",

language = "English",

series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",

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Shou, Z, Jiang, Y & Lin, F 2022, AMR-DA: Data Augmentation by Abstract Meaning Representation. in S Muresan, P Nakov & A Villavicencio (eds), ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Findings of ACL 2022. Proceedings of the Annual Meeting of the Association for Computational Linguistics, Association for Computational Linguistics (ACL), pp. 3082-3098, Findings of the Association for Computational Linguistics: ACL 2022, Dublin, Ireland, 22/05/22. https://doi.org/10.18653/v1/2022.findings-acl.244

AMR-DA: Data Augmentation by Abstract Meaning Representation. / Shou, Ziyi; Jiang, Yuxin; Lin, Fangzhen.
ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Findings of ACL 2022. ed. / Smaranda Muresan; Preslav Nakov; Aline Villavicencio. Association for Computational Linguistics (ACL), 2022. p. 3082-3098 (Proceedings of the Annual Meeting of the Association for Computational Linguistics).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

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T1 - AMR-DA

T2 - Findings of the Association for Computational Linguistics: ACL 2022

AU - Shou, Ziyi

AU - Jiang, Yuxin

AU - Lin, Fangzhen

PY - 2022

Y1 - 2022

N2 - Meaning Representation (AMR) is a semantic representation for NLP/NLU. In this paper, we propose to use it for data augmentation in NLP. Our proposed data augmentation technique, called AMR-DA, converts a sample sentence to an AMR graph, modifies the graph according to various data augmentation policies, and then generates augmentations from graphs. Our method combines both sentence-level techniques like back translation and token-level techniques like EDA (Easy Data Augmentation). To evaluate the effectiveness of our method, we apply it to the English tasks of semantic textual similarity (STS) and text classification. For STS, our experiments show that AMR-DA boosts the performance of the state-of-the-art models on several STS benchmarks. For text classification, AMR-DA outperforms EDA and AEDA and leads to more robust improvements.

AB - Meaning Representation (AMR) is a semantic representation for NLP/NLU. In this paper, we propose to use it for data augmentation in NLP. Our proposed data augmentation technique, called AMR-DA, converts a sample sentence to an AMR graph, modifies the graph according to various data augmentation policies, and then generates augmentations from graphs. Our method combines both sentence-level techniques like back translation and token-level techniques like EDA (Easy Data Augmentation). To evaluate the effectiveness of our method, we apply it to the English tasks of semantic textual similarity (STS) and text classification. For STS, our experiments show that AMR-DA boosts the performance of the state-of-the-art models on several STS benchmarks. For text classification, AMR-DA outperforms EDA and AEDA and leads to more robust improvements.

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M3 - Conference Paper published in a book

T3 - Proceedings of the Annual Meeting of the Association for Computational Linguistics

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Shou Z, Jiang Y, Lin F. AMR-DA: Data Augmentation by Abstract Meaning Representation. In Muresan S, Nakov P, Villavicencio A, editors, ACL 2022 - 60th Annual Meeting of the Association for Computational Linguistics, Findings of ACL 2022. Association for Computational Linguistics (ACL). 2022. p. 3082-3098. (Proceedings of the Annual Meeting of the Association for Computational Linguistics). doi: 10.18653/v1/2022.findings-acl.244

AMR-DA: Data Augmentation by Abstract Meaning Representation

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