@inproceedings{ramesh-kashyap-kan-2020-sciwing,
title = "{S}ci{WING}{--} A Software Toolkit for Scientific Document Processing",
author = "Ramesh Kashyap, Abhinav and
Kan, Min-Yen",
booktitle = "Proceedings of the First Workshop on Scholarly Document Processing",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.sdp-1.13",
doi = "10.18653/v1/2020.sdp-1.13",
pages = "113--120",
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.",
}
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%0 Conference Proceedings
%T SciWING– A Software Toolkit for Scientific Document Processing
%A Ramesh Kashyap, Abhinav
%A Kan, Min-Yen
%S Proceedings of the First Workshop on Scholarly Document Processing
%D 2020
%8 nov
%I Association for Computational Linguistics
%C Online
%F ramesh-kashyap-kan-2020-sciwing
%X 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.
%R 10.18653/v1/2020.sdp-1.13
%U https://aclanthology.org/2020.sdp-1.13
%U https://doi.org/10.18653/v1/2020.sdp-1.13
%P 113-120
Markdown (Informal)
[SciWING– A Software Toolkit for Scientific Document Processing](https://aclanthology.org/2020.sdp-1.13) (Ramesh Kashyap & Kan, sdp 2020)
ACL