Abstract
We introduce SciWING, an open-source soft-ware toolkit which provides access to state-of-the-art pre-trained models for scientific document processing (SDP) tasks, such as citation string parsing, logical structure recovery and citation intent classification. Compared to other toolkits, SciWING follows a full neural pipeline and provides a Python inter-face for SDP. When needed, SciWING provides fine-grained control for rapid experimentation with different models by swapping and stacking different modules. Transfer learning from general and scientific documents specific pre-trained transformers (i.e., BERT, SciBERT, etc.) can be performed. SciWING incorporates ready-to-use web and terminal-based applications and demonstrations to aid adoption and development. The toolkit is available from http://sciwing.io and the demos are available at http://rebrand.ly/sciwing-demo.- Anthology ID:
- 2020.sdp-1.13
- Volume:
- Proceedings of the First Workshop on Scholarly Document Processing
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Muthu Kumar Chandrasekaran, Anita de Waard, Guy Feigenblat, Dayne Freitag, Tirthankar Ghosal, Eduard Hovy, Petr Knoth, David Konopnicki, Philipp Mayr, Robert M. Patton, Michal Shmueli-Scheuer
- Venue:
- sdp
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 113–120
- Language:
- URL:
- https://aclanthology.org/2020.sdp-1.13
- DOI:
- 10.18653/v1/2020.sdp-1.13
- Cite (ACL):
- Abhinav Ramesh Kashyap and Min-Yen Kan. 2020. SciWING– A Software Toolkit for Scientific Document Processing. In Proceedings of the First Workshop on Scholarly Document Processing, pages 113–120, Online. Association for Computational Linguistics.
- Cite (Informal):
- SciWING– A Software Toolkit for Scientific Document Processing (Ramesh Kashyap & Kan, sdp 2020)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2020.sdp-1.13.pdf
- Data
- Semantic Scholar