<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="tang-etal-2018-evaluation">
    <titleInfo>
        <title>An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Gongbo</namePart>
        <namePart type="family">Tang</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Fabienne</namePart>
        <namePart type="family">Cap</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Eva</namePart>
        <namePart type="family">Pettersson</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Joakim</namePart>
        <namePart type="family">Nivre</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-08</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 27th International Conference on Computational Linguistics</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Emily</namePart>
            <namePart type="given">M</namePart>
            <namePart type="family">Bender</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Leon</namePart>
            <namePart type="family">Derczynski</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Pierre</namePart>
            <namePart type="family">Isabelle</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Santa Fe, New Mexico, USA</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages: English, German, Hungarian, Icelandic, and Swedish. The NMT models are at different levels, have different attention mechanisms, and different neural network architectures. Our results show that NMT models are much better than SMT models in terms of character error rate. The vanilla RNNs are competitive to GRUs/LSTMs in historical spelling normalization. Transformer models perform better only when provided with more training data. We also find that subword-level models with a small subword vocabulary are better than character-level models. In addition, we propose a hybrid method which further improves the performance of historical spelling normalization.</abstract>
    <identifier type="citekey">tang-etal-2018-evaluation</identifier>
    <location>
        <url>https://aclanthology.org/C18-1112</url>
    </location>
    <part>
        <date>2018-08</date>
        <extent unit="page">
            <start>1320</start>
            <end>1331</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization
%A Tang, Gongbo
%A Cap, Fabienne
%A Pettersson, Eva
%A Nivre, Joakim
%Y Bender, Emily M.
%Y Derczynski, Leon
%Y Isabelle, Pierre
%S Proceedings of the 27th International Conference on Computational Linguistics
%D 2018
%8 August
%I Association for Computational Linguistics
%C Santa Fe, New Mexico, USA
%F tang-etal-2018-evaluation
%X In this paper, we apply different NMT models to the problem of historical spelling normalization for five languages: English, German, Hungarian, Icelandic, and Swedish. The NMT models are at different levels, have different attention mechanisms, and different neural network architectures. Our results show that NMT models are much better than SMT models in terms of character error rate. The vanilla RNNs are competitive to GRUs/LSTMs in historical spelling normalization. Transformer models perform better only when provided with more training data. We also find that subword-level models with a small subword vocabulary are better than character-level models. In addition, we propose a hybrid method which further improves the performance of historical spelling normalization.
%U https://aclanthology.org/C18-1112
%P 1320-1331

Markdown (Informal)

[An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization](https://aclanthology.org/C18-1112) (Tang et al., COLING 2018)

• An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization (Tang et al., COLING 2018)

ACL

• Gongbo Tang, Fabienne Cap, Eva Pettersson, and Joakim Nivre. 2018. An Evaluation of Neural Machine Translation Models on Historical Spelling Normalization. In Proceedings of the 27th International Conference on Computational Linguistics, pages 1320–1331, Santa Fe, New Mexico, USA. Association for Computational Linguistics.