@inproceedings{chakrabarty-etal-2019-imho,
title = "{IMHO} Fine-Tuning Improves Claim Detection",
author = "Chakrabarty, Tuhin and
Hidey, Christopher and
McKeown, Kathy",
editor = "Burstein, Jill and
Doran, Christy and
Solorio, Thamar",
booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N19-1054",
doi = "10.18653/v1/N19-1054",
pages = "558--563",
abstract = "Claims are the central component of an argument. Detecting claims across different domains or data sets can often be challenging due to their varying conceptualization. We propose to alleviate this problem by fine-tuning a language model using a Reddit corpus of 5.5 million opinionated claims. These claims are self-labeled by their authors using the internet acronyms IMO/IMHO (in my (humble) opinion). Empirical results show that using this approach improves the state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points in claim detection. As these data sets include diverse domains such as social media and student essays this improvement demonstrates the robustness of fine-tuning on this novel corpus.",
}
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%0 Conference Proceedings
%T IMHO Fine-Tuning Improves Claim Detection
%A Chakrabarty, Tuhin
%A Hidey, Christopher
%A McKeown, Kathy
%Y Burstein, Jill
%Y Doran, Christy
%Y Solorio, Thamar
%S 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)
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota
%F chakrabarty-etal-2019-imho
%X Claims are the central component of an argument. Detecting claims across different domains or data sets can often be challenging due to their varying conceptualization. We propose to alleviate this problem by fine-tuning a language model using a Reddit corpus of 5.5 million opinionated claims. These claims are self-labeled by their authors using the internet acronyms IMO/IMHO (in my (humble) opinion). Empirical results show that using this approach improves the state of art performance across four benchmark argumentation data sets by an average of 4 absolute F1 points in claim detection. As these data sets include diverse domains such as social media and student essays this improvement demonstrates the robustness of fine-tuning on this novel corpus.
%R 10.18653/v1/N19-1054
%U https://aclanthology.org/N19-1054
%U https://doi.org/10.18653/v1/N19-1054
%P 558-563
Markdown (Informal)
[IMHO Fine-Tuning Improves Claim Detection](https://aclanthology.org/N19-1054) (Chakrabarty et al., NAACL 2019)
ACL
- Tuhin Chakrabarty, Christopher Hidey, and Kathy McKeown. 2019. IMHO Fine-Tuning Improves Claim Detection. In 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), pages 558–563, Minneapolis, Minnesota. Association for Computational Linguistics.