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
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
