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            <title>Proceedings of the First Workshop on Multimodal Machine Translation for Low Resource Languages (MMTLRL 2021)</title>
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        <name type="personal">
            <namePart type="given">Thoudam</namePart>
            <namePart type="family">Doren Singh</namePart>
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        <name type="personal">
            <namePart type="given">Cristina</namePart>
            <namePart type="family">España i Bonet</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Sivaji</namePart>
            <namePart type="family">Bandyopadhyay</namePart>
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            <namePart type="given">Josef</namePart>
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    <abstract>In this talk, I will describe current research directions in my group that aim to make machine translation (MT) more human-centered. Instead of viewing MT solely as a task that aims to transduce a source sentence into a well-formed target language equivalent, we revisit all steps of the MT research and development lifecycle with the goal of designing MT systems that are able to help people communicate across language barriers. I will present methods to better characterize the parallel training data that powers MT systems, and how the degree of equivalence impacts translation quality. I will introduce models that enable flexible conditional language generation, and will discuss recent work on framing machine translation tasks and evaluation to center human factors.</abstract>
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%0 Conference Proceedings
%T Models and Tasks for Human-Centered Machine Translation
%A Carpuat, Marine
%Y Doren Singh, Thoudam
%Y España i Bonet, Cristina
%Y Bandyopadhyay, Sivaji
%Y van Genabith, Josef
%S Proceedings of the First Workshop on Multimodal Machine Translation for Low Resource Languages (MMTLRL 2021)
%D 2021
%8 September
%I INCOMA Ltd.
%C Online (Virtual Mode)
%F carpuat-2021-models
%X In this talk, I will describe current research directions in my group that aim to make machine translation (MT) more human-centered. Instead of viewing MT solely as a task that aims to transduce a source sentence into a well-formed target language equivalent, we revisit all steps of the MT research and development lifecycle with the goal of designing MT systems that are able to help people communicate across language barriers. I will present methods to better characterize the parallel training data that powers MT systems, and how the degree of equivalence impacts translation quality. I will introduce models that enable flexible conditional language generation, and will discuss recent work on framing machine translation tasks and evaluation to center human factors.
%U https://aclanthology.org/2021.mmtlrl-1.1/
%P 1

Markdown (Informal)

[Models and Tasks for Human-Centered Machine Translation](https://aclanthology.org/2021.mmtlrl-1.1/) (Carpuat, MMTLRL 2021)

• Models and Tasks for Human-Centered Machine Translation (Carpuat, MMTLRL 2021)

ACL

• Marine Carpuat. 2021. Models and Tasks for Human-Centered Machine Translation. In Proceedings of the First Workshop on Multimodal Machine Translation for Low Resource Languages (MMTLRL 2021), page 1, Online (Virtual Mode). INCOMA Ltd..