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        <title>Improving historical spelling normalization with bi-directional LSTMs and multi-task learning</title>
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    <name type="personal">
        <namePart type="given">Marcel</namePart>
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    <name type="personal">
        <namePart type="given">Anders</namePart>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Rashmi</namePart>
            <namePart type="family">Prasad</namePart>
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    <abstract>Natural-language processing of historical documents is complicated by the abundance of variant spellings and lack of annotated data. A common approach is to normalize the spelling of historical words to modern forms. We explore the suitability of a deep neural network architecture for this task, particularly a deep bi-LSTM network applied on a character level. Our model compares well to previously established normalization algorithms when evaluated on a diverse set of texts from Early New High German. We show that multi-task learning with additional normalization data can improve our model’s performance further.</abstract>
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%0 Conference Proceedings
%T Improving historical spelling normalization with bi-directional LSTMs and multi-task learning
%A Bollmann, Marcel
%A Søgaard, Anders
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F bollmann-sogaard-2016-improving
%X Natural-language processing of historical documents is complicated by the abundance of variant spellings and lack of annotated data. A common approach is to normalize the spelling of historical words to modern forms. We explore the suitability of a deep neural network architecture for this task, particularly a deep bi-LSTM network applied on a character level. Our model compares well to previously established normalization algorithms when evaluated on a diverse set of texts from Early New High German. We show that multi-task learning with additional normalization data can improve our model’s performance further.
%U https://aclanthology.org/C16-1013
%P 131-139

Markdown (Informal)

[Improving historical spelling normalization with bi-directional LSTMs and multi-task learning](https://aclanthology.org/C16-1013) (Bollmann & Søgaard, COLING 2016)

• Improving historical spelling normalization with bi-directional LSTMs and multi-task learning (Bollmann & Søgaard, COLING 2016)

ACL

• Marcel Bollmann and Anders Søgaard. 2016. Improving historical spelling normalization with bi-directional LSTMs and multi-task learning. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 131–139, Osaka, Japan. The COLING 2016 Organizing Committee.