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Article . 2022
License: CC BY
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Efficient Argument Structure Extraction with Transfer Learning and Active Learning

Authors: Xinyu Hua; Lu Wang;

Efficient Argument Structure Extraction with Transfer Learning and Active Learning

Abstract

The automation of extracting argument structures faces a pair of challenges on (1) encoding long-term contexts to facilitate comprehensive understanding, and (2) improving data efficiency since constructing high-quality argument structures is time-consuming. In this work, we propose a novel context-aware Transformer-based argument structure prediction model which, on five different domains, significantly outperforms models that rely on features or only encode limited contexts. To tackle the difficulty of data annotation, we examine two complementary methods: (i) transfer learning to leverage existing annotated data to boost model performance in a new target domain, and (ii) active learning to strategically identify a small amount of samples for annotation. We further propose model-independent sample acquisition strategies, which can be generalized to diverse domains. With extensive experiments, we show that our simple-yet-effective acquisition strategies yield competitive results against three strong comparisons. Combined with transfer learning, substantial F1 score boost (5-25) can be further achieved during the early iterations of active learning across domains.

Comment: Findings of ACL 2022, long paper

Keywords

FOS: Computer and information sciences, Computer Science - Computation and Language, argument structure prediction, argument relation extraction, argument mining, peer review, Computation and Language (cs.CL)

58 references, page 1 of 6

Pablo Accuosto and Horacio Saggion. 2019. Transferring knowledge from discourse to arguments: A case study with scientific abstracts. In Proceedings of the 6th Workshop on Argument Mining, pages 41- 51, Florence, Italy. Association for Computational Linguistics.

Charu C Aggarwal, Xiangnan Kong, Quanquan Gu, Jiawei Han, and S Yu Philip. 2014. Active learning: A survey. In Data Classification: Algorithms and Applications, pages 571-605. CRC Press.

Khalid Al Khatib, Tirthankar Ghosal, Yufang Hou, Anita de Waard, and Dayne Freitag. 2021. Argument mining for scholarly document processing: Taking stock and looking ahead. In Proceedings of the Second Workshop on Scholarly Document Processing, pages 56-65, Online. Association for Computational Linguistics.

Iz Beltagy, Kyle Lo, and Arman Cohan. 2019. SciBERT: A pretrained language model for scientific text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3615- 3620, Hong Kong, China. Association for Computational Linguistics.

Zalán Bodó, Zsolt Minier, and Lehel Csató. 2011. Active learning with clustering. In Active Learning and Experimental Design workshop In conjunction with AISTATS 2010, volume 16 of Proceedings of Machine Learning Research, pages 127-139, Sardinia, Italy. PMLR.

Filip Boltužic´ and Jan Šnajder. 2014. Back up your stance: Recognizing arguments in online discussions. In Proceedings of the First Workshop on Argumentation Mining, pages 49-58, Baltimore, Maryland. Association for Computational Linguistics.

Elena Cabrio and Serena Villata. 2018. Five years of argument mining: a data-driven analysis. In IJCAI, volume 18, pages 5427-5433. [OpenAIRE]

Claire Cardie, Cynthia Farina, Matt Rawding, and Adil Aijaz. 2008. An eRulemaking corpus: Identifying substantive issues in public comments. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08), Marrakech, Morocco. European Language Resources Association (ELRA).

Tuhin Chakrabarty, Christopher Hidey, Smaranda Muresan, Kathy McKeown, and Alyssa Hwang. 2019. AMPERSAND: Argument mining for PERSuAsive oNline discussions. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 2933-2943, Hong Kong, China. Association for Computational Linguistics.

Arman Cohan, Franck Dernoncourt, Doo Soon Kim, Trung Bui, Seokhwan Kim, Walter Chang, and Nazli Goharian. 2018. A discourse-aware attention model for abstractive summarization of long documents. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 615-621, New Orleans, Louisiana. Association for Computational Linguistics. [OpenAIRE]

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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Funded by
NSF| RI: Small: Collaborative Research: Computational Methods for Argument Mining: Extraction, Aggregation, and Generation
Project
  • Funder: National Science Foundation (NSF)
  • Project Code: 2100885
  • Funding stream: Directorate for Computer & Information Science & Engineering | Division of Information and Intelligent Systems
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