Multitask Learning for Citation Purpose Classification
Yasa M. Baig, Alex X. Oesterling, Rui Xin, Haoyang Yu, Angikar Ghosal, Lesia Semenova, Cynthia Rudin
Abstract
We present our entry into the 2021 3C Shared Task Citation Context Classification based on Purpose competition. The goal of the competition is to classify a citation in a scientific article based on its purpose. This task is important because it could potentially lead to more comprehensive ways of summarizing the purpose and uses of scientific articles, but it is also difficult, mainly due to the limited amount of available training data in which the purposes of each citation have been hand-labeled, along with the subjectivity of these labels. Our entry in the competition is a multi-task model that combines multiple modules designed to handle the problem from different perspectives, including hand-generated linguistic features, TF-IDF features, and an LSTM-with- attention model. We also provide an ablation study and feature analysis whose insights could lead to future work.- Anthology ID:
- 2021.sdp-1.18
- Volume:
- Proceedings of the Second Workshop on Scholarly Document Processing
- Month:
- June
- Year:
- 2021
- Address:
- Online
- Editors:
- Iz Beltagy, Arman Cohan, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Keith Hall, Drahomira Herrmannova, Petr Knoth, Kyle Lo, Philipp Mayr, Robert M. Patton, Michal Shmueli-Scheuer, Anita de Waard, Kuansan Wang, Lucy Lu Wang
- Venue:
- sdp
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 134–139
- Language:
- URL:
- https://aclanthology.org/2021.sdp-1.18
- DOI:
- Bibkey:
- Cite (ACL):
- Yasa M. Baig, Alex X. Oesterling, Rui Xin, Haoyang Yu, Angikar Ghosal, Lesia Semenova, and Cynthia Rudin. 2021. Multitask Learning for Citation Purpose Classification. In Proceedings of the Second Workshop on Scholarly Document Processing, pages 134–139, Online. Association for Computational Linguistics.
- Cite (Informal):
- Multitask Learning for Citation Purpose Classification (Baig et al., sdp 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.sdp-1.18.pdf
Export citation
@inproceedings{baig-etal-2021-multitask, title = "Multitask Learning for Citation Purpose Classification", author = "Baig, Yasa M. and Oesterling, Alex X. and Xin, Rui and Yu, Haoyang and Ghosal, Angikar and Semenova, Lesia and Rudin, Cynthia", editor = "Beltagy, Iz and Cohan, Arman and Feigenblat, Guy and Freitag, Dayne and Ghosal, Tirthankar and Hall, Keith and Herrmannova, Drahomira and Knoth, Petr and Lo, Kyle and Mayr, Philipp and Patton, Robert M. and Shmueli-Scheuer, Michal and de Waard, Anita and Wang, Kuansan and Wang, Lucy Lu", booktitle = "Proceedings of the Second Workshop on Scholarly Document Processing", month = jun, year = "2021", address = "Online", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2021.sdp-1.18", pages = "134--139", abstract = "We present our entry into the 2021 3C Shared Task Citation Context Classification based on Purpose competition. The goal of the competition is to classify a citation in a scientific article based on its purpose. This task is important because it could potentially lead to more comprehensive ways of summarizing the purpose and uses of scientific articles, but it is also difficult, mainly due to the limited amount of available training data in which the purposes of each citation have been hand-labeled, along with the subjectivity of these labels. Our entry in the competition is a multi-task model that combines multiple modules designed to handle the problem from different perspectives, including hand-generated linguistic features, TF-IDF features, and an LSTM-with- attention model. We also provide an ablation study and feature analysis whose insights could lead to future work.", }
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%0 Conference Proceedings %T Multitask Learning for Citation Purpose Classification %A Baig, Yasa M. %A Oesterling, Alex X. %A Xin, Rui %A Yu, Haoyang %A Ghosal, Angikar %A Semenova, Lesia %A Rudin, Cynthia %Y Beltagy, Iz %Y Cohan, Arman %Y Feigenblat, Guy %Y Freitag, Dayne %Y Ghosal, Tirthankar %Y Hall, Keith %Y Herrmannova, Drahomira %Y Knoth, Petr %Y Lo, Kyle %Y Mayr, Philipp %Y Patton, Robert M. %Y Shmueli-Scheuer, Michal %Y de Waard, Anita %Y Wang, Kuansan %Y Wang, Lucy Lu %S Proceedings of the Second Workshop on Scholarly Document Processing %D 2021 %8 June %I Association for Computational Linguistics %C Online %F baig-etal-2021-multitask %X We present our entry into the 2021 3C Shared Task Citation Context Classification based on Purpose competition. The goal of the competition is to classify a citation in a scientific article based on its purpose. This task is important because it could potentially lead to more comprehensive ways of summarizing the purpose and uses of scientific articles, but it is also difficult, mainly due to the limited amount of available training data in which the purposes of each citation have been hand-labeled, along with the subjectivity of these labels. Our entry in the competition is a multi-task model that combines multiple modules designed to handle the problem from different perspectives, including hand-generated linguistic features, TF-IDF features, and an LSTM-with- attention model. We also provide an ablation study and feature analysis whose insights could lead to future work. %U https://aclanthology.org/2021.sdp-1.18 %P 134-139
Markdown (Informal)
[Multitask Learning for Citation Purpose Classification](https://aclanthology.org/2021.sdp-1.18) (Baig et al., sdp 2021)
- Multitask Learning for Citation Purpose Classification (Baig et al., sdp 2021)
ACL
- Yasa M. Baig, Alex X. Oesterling, Rui Xin, Haoyang Yu, Angikar Ghosal, Lesia Semenova, and Cynthia Rudin. 2021. Multitask Learning for Citation Purpose Classification. In Proceedings of the Second Workshop on Scholarly Document Processing, pages 134–139, Online. Association for Computational Linguistics.