The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
IMHO Fine-Tuning Improves Claim Detection
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“Prior work has shown the benefit of continued pretraining in domain (Alsentzer et al., 2019; Chakrabarty et al., 2019; Lee et al., 2019).”
From Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks (2020) · cites this paper as Chakrabarty et al., 2019
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1423 · Cited by 54,325
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983
Universal Language Model Fine-tuning for Text Classification
Howard, J., & Ruder, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1031 · Cited by 4,100
Research works printed but not linked: 15
- Khalid Al-Khatib, Henning Wachsmuth, Matthias Ha- gen, Jonas Kohler, and Benno Stein. 2016. Do- main mining of argumentative text through distant supervision. In 15th Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1395-1404.
- Johannes Daxenberger, Steffen Eger, Ivan Habernal, Christian Stab, and Iryna Gurevych. 2017. What is the essence of a claim? cross-domain claim identifi- cation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Process- ing, pages 2055-2066.
- Steffen Eger, Johannes Daxenberger, and Iryna Gurevych. 2017. Neural end-to-end learning for computational argumentation mining. In In Pro- ceedings of the 55th Annual Meeting of the Associa- tion for Computational Linguistics., pages 11-22.
- Trudy Govier. 2010. A Practical Study of Argument. 7th edition Cengage Learning, Wadsworth.
- Ivan Habernal and Iryna Gurevych. 2017. Argumen- tation mining in user-generated web discourse. In Computational Linguistics, 43(1), pages 125-179.
- Christopher Hidey, Elena Musi, Alyssa Hwang, Smaranda Muresan, and Kathleen McKeown. 2017. Analyzing the semantic types of claims and premises in an online persuasive forum. In In Pro- ceedings of the 4th Workshop on Argument Mining. EMNLP, pages 11-21.
- Stephen Merity, Caiming Xiong, James Bradbury, and Richard Socher. 2017. In proceedings of the inter- national conference on learning representations.
- Andreas Peldszus and Manfred Stede. 2016. An an- notated corpus of argumentative microtexts. In In Argumentation and Reasoned Action: Proceedings of the 1st European Conference on Argumentation, pages 810-815.
- Isaac Persing and Vincent Ng. 2016. End-to-end ar- gumentation mining in student essays. In In Pro- ceedings of the 15th Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies., pages 1384-1394.
- Matthew E. Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word rep- resentations. In Proc. of NAACL.
- Sara Rosenthal and Kathy McKeown. 2012. Detecting opinionated claims in online discussion. In Inpro- ceedings of the Sixth IEEE International Conference on Semantic Computing (IEEE ICSC2012) Special Session on Semantics and Sociolinguistics in Social Medi.
- Claudia Schulz, Steffen Eger, Johannes Daxenberger, Tobias Kahse, and Iryna Gurevych. 2018. Multi- task learning for argumentation mining in low- resource settings. In Proceedings of NAACL-HLT 2018, pages 35-41.
- Christian Stab and Iryna Gurevych. 2017. Parsing argumentation structures in persuasive essays. In Computational Linguistics, pages in press, preprint available at arXiv:1604.07370. · arXiv
- Stephen E. Toulmin. 2003. The Uses of Argument. Cambridge University Press, New York.
- Zhilin Yang, Jake Zhao, Bhuwan Dhingra, Kaim- ing He, William W. Cohen, Ruslan Salakhutdi- nov, and Yann LeCun. 2018. Glomo: Unsupervis- edly learned relational graphs as transferable. In arXiv:1806.05662. · arXiv
Cited by
Sorted by citations
Don’t Stop Pretraining: Adapt Language Models to Domains and Tasks
Gururangan, S., Marasović, A., Swayamdipta, S., Lo, K., Beltagy, I., Downey, D., & Smith, N. A. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.740 · Cited by 2,103
Metadata
- Authors
- Chakrabarty, T., Hidey, C., & McKeown, K.
- Venue
- Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
- Published
- 2019
- Pages
- 558-563
- Cited by
- 9
- References
- 3
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- The paper
- Chakrabarty, T., Hidey, C., & McKeown, K. (2019). IMHO Fine-Tuning Improves Claim Detection. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). https://doi.org/10.18653/v1/n19-1054
