<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="haq-etal-2025-audio">
    <titleInfo>
        <title>Audio-Based Crowd-Sourced Evaluation of Machine Translation Quality</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Sami</namePart>
        <namePart type="family">Haq</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Sheila</namePart>
        <namePart type="family">Castilho</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Yvette</namePart>
        <namePart type="family">Graham</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2025-11</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Tenth Conference on Machine Translation</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Barry</namePart>
            <namePart type="family">Haddow</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Tom</namePart>
            <namePart type="family">Kocmi</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Philipp</namePart>
            <namePart type="family">Koehn</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Christof</namePart>
            <namePart type="family">Monz</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>Association for Computational Linguistics</publisher>
            <place>
                <placeTerm type="text">Suzhou, China</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-341-8</identifier>
    </relatedItem>
    <abstract>Machine Translation (MT) has achieved remarkable performance, with growing interest in speech translation and multimodal approaches. However, despite these advancements, MT quality assessment remains largely text-centric, typically relying on human experts who read and compare texts. Since many real-world MT applications (e.g., Google Translate Voice Mode, iFLYTEK Translator) involve translation being spoken rather printed or read, a more natural way to assess translation quality would be through speech as opposed text-only evaluations. This study compares text-only and audio-based evaluations of 10 MT systems from the WMT General MT Shared Task, using crowd-sourced judgments collected via Amazon Mechanical Turk. We additionally, performed statistical significance testing and self-replication experiments to test reliability and consistency of audio-based approach. Crowd-sourced assessments based on audio yield rankings largely consistent with text-only evaluations but, in some cases, identify significant differences between translation systems. We attribute this to speech’s richer, more natural modality and propose incorporating speech-based assessments into future MT evaluation frameworks.</abstract>
    <identifier type="citekey">haq-etal-2025-audio</identifier>
    <location>
        <url>https://aclanthology.org/2025.wmt-1.3/</url>
    </location>
    <part>
        <date>2025-11</date>
        <extent unit="page">
            <start>52</start>
            <end>63</end>
        </extent>
    </part>
</mods>
</modsCollection>

%0 Conference Proceedings
%T Audio-Based Crowd-Sourced Evaluation of Machine Translation Quality
%A Haq, Sami
%A Castilho, Sheila
%A Graham, Yvette
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Tenth Conference on Machine Translation
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-341-8
%F haq-etal-2025-audio
%X Machine Translation (MT) has achieved remarkable performance, with growing interest in speech translation and multimodal approaches. However, despite these advancements, MT quality assessment remains largely text-centric, typically relying on human experts who read and compare texts. Since many real-world MT applications (e.g., Google Translate Voice Mode, iFLYTEK Translator) involve translation being spoken rather printed or read, a more natural way to assess translation quality would be through speech as opposed text-only evaluations. This study compares text-only and audio-based evaluations of 10 MT systems from the WMT General MT Shared Task, using crowd-sourced judgments collected via Amazon Mechanical Turk. We additionally, performed statistical significance testing and self-replication experiments to test reliability and consistency of audio-based approach. Crowd-sourced assessments based on audio yield rankings largely consistent with text-only evaluations but, in some cases, identify significant differences between translation systems. We attribute this to speech’s richer, more natural modality and propose incorporating speech-based assessments into future MT evaluation frameworks.
%U https://aclanthology.org/2025.wmt-1.3/
%P 52-63

Markdown (Informal)

[Audio-Based Crowd-Sourced Evaluation of Machine Translation Quality](https://aclanthology.org/2025.wmt-1.3/) (Haq et al., WMT 2025)

• Audio-Based Crowd-Sourced Evaluation of Machine Translation Quality (Haq et al., WMT 2025)

ACL

• Sami Haq, Sheila Castilho, and Yvette Graham. 2025. Audio-Based Crowd-Sourced Evaluation of Machine Translation Quality. In Proceedings of the Tenth Conference on Machine Translation, pages 52–63, Suzhou, China. Association for Computational Linguistics.