<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="bollmann-etal-2018-multi">
    <titleInfo>
        <title>Multi-task learning for historical text normalization: Size matters</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Marcel</namePart>
        <namePart type="family">Bollmann</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Anders</namePart>
        <namePart type="family">Søgaard</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Joachim</namePart>
        <namePart type="family">Bingel</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2018-07</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Reza</namePart>
            <namePart type="family">Haffari</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Colin</namePart>
            <namePart type="family">Cherry</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">George</namePart>
            <namePart type="family">Foster</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Shahram</namePart>
            <namePart type="family">Khadivi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Bahar</namePart>
            <namePart type="family">Salehi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Melbourne</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Historical text normalization suffers from small datasets that exhibit high variance, and previous work has shown that multi-task learning can be used to leverage data from related problems in order to obtain more robust models. Previous work has been limited to datasets from a specific language and a specific historical period, and it is not clear whether results generalize. It therefore remains an open problem, when historical text normalization benefits from multi-task learning. We explore the benefits of multi-task learning across 10 different datasets, representing different languages and periods. Our main finding—contrary to what has been observed for other NLP tasks—is that multi-task learning mainly works when target task data is very scarce.</abstract>
    <identifier type="citekey">bollmann-etal-2018-multi</identifier>
    <identifier type="doi">10.18653/v1/W18-3403</identifier>
    <location>
        <url>https://aclanthology.org/W18-3403</url>
    </location>
    <part>
        <date>2018-07</date>
        <extent unit="page">
            <start>19</start>
            <end>24</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Multi-task learning for historical text normalization: Size matters
%A Bollmann, Marcel
%A Søgaard, Anders
%A Bingel, Joachim
%Y Haffari, Reza
%Y Cherry, Colin
%Y Foster, George
%Y Khadivi, Shahram
%Y Salehi, Bahar
%S Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne
%F bollmann-etal-2018-multi
%X Historical text normalization suffers from small datasets that exhibit high variance, and previous work has shown that multi-task learning can be used to leverage data from related problems in order to obtain more robust models. Previous work has been limited to datasets from a specific language and a specific historical period, and it is not clear whether results generalize. It therefore remains an open problem, when historical text normalization benefits from multi-task learning. We explore the benefits of multi-task learning across 10 different datasets, representing different languages and periods. Our main finding—contrary to what has been observed for other NLP tasks—is that multi-task learning mainly works when target task data is very scarce.
%R 10.18653/v1/W18-3403
%U https://aclanthology.org/W18-3403
%U https://doi.org/10.18653/v1/W18-3403
%P 19-24

Markdown (Informal)

[Multi-task learning for historical text normalization: Size matters](https://aclanthology.org/W18-3403) (Bollmann et al., ACL 2018)

• Multi-task learning for historical text normalization: Size matters (Bollmann et al., ACL 2018)

ACL

• Marcel Bollmann, Anders Søgaard, and Joachim Bingel. 2018. Multi-task learning for historical text normalization: Size matters. In Proceedings of the Workshop on Deep Learning Approaches for Low-Resource NLP, pages 19–24, Melbourne. Association for Computational Linguistics.