<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="sarti-bisazza-2022-indeep">
    <titleInfo>
        <title>InDeep \times NMT: Empowering Human Translators via Interpretable Neural Machine Translation</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Gabriele</namePart>
        <namePart type="family">Sarti</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Arianna</namePart>
        <namePart type="family">Bisazza</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2022-06</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the 23rd Annual Conference of the European Association for Machine Translation</title>
        </titleInfo>
        <originInfo>
            <publisher>European Association for Machine Translation</publisher>
            <place>
                <placeTerm type="text">Ghent, Belgium</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Neural machine translation (NMT) systems are nowadays essential components of professional translation workflows. Consequently, human translators are increasingly working as post-editors for machine-translated content. The NWO-funded InDeep project aims to empower users of Deep Learning models of text, speech, and music by improving their ability to interact with such models and interpret their behaviors. In the specific context of translation, we aim at developing new tools and methodologies to improve prediction attribution, error analysis, and controllable generation for NMT systems. These advances will be evaluated through field studies involving professional translators to assess gains in efficiency and overall enjoyability of the post-editing process.</abstract>
    <identifier type="citekey">sarti-bisazza-2022-indeep</identifier>
    <location>
        <url>https://aclanthology.org/2022.eamt-1.46</url>
    </location>
    <part>
        <date>2022-06</date>
        <extent unit="page">
            <start>313</start>
            <end>314</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T InDeep \times NMT: Empowering Human Translators via Interpretable Neural Machine Translation
%A Sarti, Gabriele
%A Bisazza, Arianna
%S Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
%D 2022
%8 June
%I European Association for Machine Translation
%C Ghent, Belgium
%F sarti-bisazza-2022-indeep
%X Neural machine translation (NMT) systems are nowadays essential components of professional translation workflows. Consequently, human translators are increasingly working as post-editors for machine-translated content. The NWO-funded InDeep project aims to empower users of Deep Learning models of text, speech, and music by improving their ability to interact with such models and interpret their behaviors. In the specific context of translation, we aim at developing new tools and methodologies to improve prediction attribution, error analysis, and controllable generation for NMT systems. These advances will be evaluated through field studies involving professional translators to assess gains in efficiency and overall enjoyability of the post-editing process.
%U https://aclanthology.org/2022.eamt-1.46
%P 313-314

Markdown (Informal)

[InDeep × NMT: Empowering Human Translators via Interpretable Neural Machine Translation](https://aclanthology.org/2022.eamt-1.46) (Sarti & Bisazza, EAMT 2022)

• InDeep × NMT: Empowering Human Translators via Interpretable Neural Machine Translation (Sarti & Bisazza, EAMT 2022)

ACL

• Gabriele Sarti and Arianna Bisazza. 2022. InDeep × NMT: Empowering Human Translators via Interpretable Neural Machine Translation. In Proceedings of the 23rd Annual Conference of the European Association for Machine Translation, pages 313–314, Ghent, Belgium. European Association for Machine Translation.