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        <title>Semi-supervised Contextual Historical Text Normalization</title>
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            <namePart type="given">Dan</namePart>
            <namePart type="family">Jurafsky</namePart>
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    <abstract>Historical text normalization, the task of mapping historical word forms to their modern counterparts, has recently attracted a lot of interest (Bollmann, 2019; Tang et al., 2018; Lusetti et al., 2018; Bollmann et al., 2018;Robertson and Goldwater, 2018; Bollmannet al., 2017; Korchagina, 2017). Yet, virtually all approaches suffer from the two limitations: 1) They consider a fully supervised setup, often with impractically large manually normalized datasets; 2) Normalization happens on words in isolation. By utilizing a simple generative normalization model and obtaining powerful contextualization from the target-side language model, we train accurate models with unlabeled historical data. In realistic training scenarios, our approach often leads to reduction in manually normalized data at the same accuracy levels.</abstract>
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%0 Conference Proceedings
%T Semi-supervised Contextual Historical Text Normalization
%A Makarov, Peter
%A Clematide, Simon
%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 makarov-clematide-2020-semi
%X Historical text normalization, the task of mapping historical word forms to their modern counterparts, has recently attracted a lot of interest (Bollmann, 2019; Tang et al., 2018; Lusetti et al., 2018; Bollmann et al., 2018;Robertson and Goldwater, 2018; Bollmannet al., 2017; Korchagina, 2017). Yet, virtually all approaches suffer from the two limitations: 1) They consider a fully supervised setup, often with impractically large manually normalized datasets; 2) Normalization happens on words in isolation. By utilizing a simple generative normalization model and obtaining powerful contextualization from the target-side language model, we train accurate models with unlabeled historical data. In realistic training scenarios, our approach often leads to reduction in manually normalized data at the same accuracy levels.
%R 10.18653/v1/2020.acl-main.650
%U https://aclanthology.org/2020.acl-main.650
%U https://doi.org/10.18653/v1/2020.acl-main.650
%P 7284-7295

Markdown (Informal)

[Semi-supervised Contextual Historical Text Normalization](https://aclanthology.org/2020.acl-main.650) (Makarov & Clematide, ACL 2020)

• Semi-supervised Contextual Historical Text Normalization (Makarov & Clematide, ACL 2020)

ACL

• Peter Makarov and Simon Clematide. 2020. Semi-supervised Contextual Historical Text Normalization. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7284–7295, Online. Association for Computational Linguistics.