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        <title>Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing</title>
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    <abstract>In this work, we focus on low-resource dependency parsing for multiple languages. Several strategies are tailored to enhance performance in low-resource scenarios. While these are well-known to the community, it is not trivial to select the best-performing combination of these strategies for a low-resource language that we are interested in, and not much attention has been given to measuring the efficacy of these strategies. We experiment with 5 low-resource strategies for our ensembled approach on 7 Universal Dependency (UD) low-resource languages. Our exhaustive experimentation on these languages supports the effective improvements for languages not covered in pretrained models. We show a successful application of the ensembled system on a truly low-resource language Sanskrit. The code and data are available at: https://github.com/Jivnesh/SanDP</abstract>
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%0 Conference Proceedings
%T Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing
%A Sandhan, Jivnesh
%A Behera, Laxmidhar
%A Goyal, Pawan
%Y Vlachos, Andreas
%Y Augenstein, Isabelle
%S Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics
%D 2023
%8 May
%I Association for Computational Linguistics
%C Dubrovnik, Croatia
%F sandhan-etal-2023-systematic
%X In this work, we focus on low-resource dependency parsing for multiple languages. Several strategies are tailored to enhance performance in low-resource scenarios. While these are well-known to the community, it is not trivial to select the best-performing combination of these strategies for a low-resource language that we are interested in, and not much attention has been given to measuring the efficacy of these strategies. We experiment with 5 low-resource strategies for our ensembled approach on 7 Universal Dependency (UD) low-resource languages. Our exhaustive experimentation on these languages supports the effective improvements for languages not covered in pretrained models. We show a successful application of the ensembled system on a truly low-resource language Sanskrit. The code and data are available at: https://github.com/Jivnesh/SanDP
%R 10.18653/v1/2023.eacl-main.158
%U https://aclanthology.org/2023.eacl-main.158
%U https://doi.org/10.18653/v1/2023.eacl-main.158
%P 2164-2171

Markdown (Informal)

[Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing](https://aclanthology.org/2023.eacl-main.158) (Sandhan et al., EACL 2023)

• Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing (Sandhan et al., EACL 2023)

ACL

• Jivnesh Sandhan, Laxmidhar Behera, and Pawan Goyal. 2023. Systematic Investigation of Strategies Tailored for Low-Resource Settings for Low-Resource Dependency Parsing. In Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pages 2164–2171, Dubrovnik, Croatia. Association for Computational Linguistics.