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        <title>Low Resource Sequence Tagging using Sentence Reconstruction</title>
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            <title>Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics</title>
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            <namePart type="given">Dan</namePart>
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    <abstract>This work revisits the task of training sequence tagging models with limited resources using transfer learning. We investigate several proposed approaches introduced in recent works and suggest a new loss that relies on sentence reconstruction from normalized embeddings. Specifically, our method demonstrates how by adding a decoding layer for sentence reconstruction, we can improve the performance of various baselines. We show improved results on the CoNLL02 NER and UD 1.2 POS datasets and demonstrate the power of the method for transfer learning with low-resources achieving 0.6 F1 score in Dutch using only one sample from it.</abstract>
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%0 Conference Proceedings
%T Low Resource Sequence Tagging using Sentence Reconstruction
%A Perl, Tal
%A Chaudhury, Sriram
%A Giryes, Raja
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F perl-etal-2020-low
%X This work revisits the task of training sequence tagging models with limited resources using transfer learning. We investigate several proposed approaches introduced in recent works and suggest a new loss that relies on sentence reconstruction from normalized embeddings. Specifically, our method demonstrates how by adding a decoding layer for sentence reconstruction, we can improve the performance of various baselines. We show improved results on the CoNLL02 NER and UD 1.2 POS datasets and demonstrate the power of the method for transfer learning with low-resources achieving 0.6 F1 score in Dutch using only one sample from it.
%R 10.18653/v1/2020.acl-main.239
%U https://aclanthology.org/2020.acl-main.239
%U https://doi.org/10.18653/v1/2020.acl-main.239
%P 2692-2698

Markdown (Informal)

[Low Resource Sequence Tagging using Sentence Reconstruction](https://aclanthology.org/2020.acl-main.239) (Perl et al., ACL 2020)

• Low Resource Sequence Tagging using Sentence Reconstruction (Perl et al., ACL 2020)

ACL

• Tal Perl, Sriram Chaudhury, and Raja Giryes. 2020. Low Resource Sequence Tagging using Sentence Reconstruction. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 2692–2698, Online. Association for Computational Linguistics.