FECE: A Challenge Set for Evaluating Fine-Grained Translation Error Correction

Jie Ding; Xiangyu Duan (段湘煜)

FECE: A Challenge Set for Evaluating Fine-Grained Translation Error Correction

Correct Metadata for

Use this form to create a GitHub issue with structured data describing the correction. You will need a GitHub account. Once you create that issue, the correction will be reviewed by a staff member.

⚠️ Mobile Users: Submitting this form to create a new issue will only work with github.com, not the GitHub Mobile app.

Important: The Anthology treat PDFs as authoritative. Please use this form only to correct data that is out of line with the PDF. See our corrections guidelines if you need to change the PDF.

Title Adjust the title. Retain tags such as <fixed-case>.

Authors Adjust author names and order to match the PDF.

Abstract Correct abstract if needed. Retain XML formatting tags such as <tex-math>. You may use ... for bold, ... for italic, ... for underline, <sc>...</sc> for small-caps, <tt>...<tt> for typewriter text, <url>...</url> for URLs, <a href=...> for hyperlinks, and <par/> for paragraph breaks.

Verification against PDF Ensure that the new title/authors match the snapshot below. (If there is no snapshot or it is too small, consult the PDF.)

Authors concatenated from the text boxes above:

ALL author names match the snapshot above—including middle initials, hyphens, and accents.

Abstract

Existing machine translation evaluation metrics assess translation quality only at the segment level and therefore cannot assess fine-grained translation quality. For translation error correction systems, this means that the quality of fine-grained corrections cannot be evaluated using existing metrics. To highlight this evaluation challenge, we propose the FECE dataset for evaluating fine-grained correction quality. Experiments on FECE show that existing metrics do not always reflect fine-grained correction performance. To enable the evaluation of fine-grained correction performance, we further propose COMET_Fine, a fine-grained evaluation metric. COMET_Fine can effectively assess fine-grained correction quality while also revealing inconsistencies between existing metrics and COMET_Fine.

Anthology ID:: 2026.wmt-main.95
Volume:: Proceedings of the Eleventh Conference on Machine Translation
Month:: October
Year:: 2026
Address:: Budapest, Hungary
Editors:: Barry Haddow, Tom Kocmi, Philipp Koehn, Christof Monz
Venues:: WMT | WS
SIG:: SIGMT
Publisher:: Association for Computational Linguistics
Note:
Pages:: 1624–1637
Language:
URL:: https://aclanthology.org/2026.wmt-main.95/
DOI:
Bibkey:
Cite (ACL):: Jie Ding and Xiangyu Duan. 2026. FECE: A Challenge Set for Evaluating Fine-Grained Translation Error Correction. In Proceedings of the Eleventh Conference on Machine Translation, pages 1624–1637, Budapest, Hungary. Association for Computational Linguistics.
Cite (Informal):: FECE: A Challenge Set for Evaluating Fine-Grained Translation Error Correction (Ding & Duan, WMT 2026)
Copy Citation:
PDF:: https://aclanthology.org/2026.wmt-main.95.pdf

PDF Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{ding-duan-2026-fece,
    title = "{FECE}: A Challenge Set for Evaluating Fine-Grained Translation Error Correction",
    author = "Ding, Jie  and
      Duan, Xiangyu",
    editor = "Haddow, Barry  and
      Kocmi, Tom  and
      Koehn, Philipp  and
      Monz, Christof",
    booktitle = "Proceedings of the Eleventh Conference on Machine Translation",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.wmt-main.95/",
    pages = "1624--1637",
    ISBN = "979-8-89176-474-3",
    abstract = "Existing machine translation evaluation metrics assess translation quality only at the segment level and therefore cannot assess fine-grained translation quality. For translation error correction systems, this means that the quality of fine-grained corrections cannot be evaluated using existing metrics. To highlight this evaluation challenge, we propose the FECE dataset for evaluating fine-grained correction quality. Experiments on FECE show that existing metrics do not always reflect fine-grained correction performance. To enable the evaluation of fine-grained correction performance, we further propose COMET{\_}Fine, a fine-grained evaluation metric. COMET{\_}Fine can effectively assess fine-grained correction quality while also revealing inconsistencies between existing metrics and COMET{\_}Fine."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="ding-duan-2026-fece">
    <titleInfo>
        <title>FECE: A Challenge Set for Evaluating Fine-Grained Translation Error Correction</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Jie</namePart>
        <namePart type="family">Ding</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Xiangyu</namePart>
        <namePart type="family">Duan</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-10</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Eleventh 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">Budapest, Hungary</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
        <identifier type="isbn">979-8-89176-474-3</identifier>
    </relatedItem>
    <abstract>Existing machine translation evaluation metrics assess translation quality only at the segment level and therefore cannot assess fine-grained translation quality. For translation error correction systems, this means that the quality of fine-grained corrections cannot be evaluated using existing metrics. To highlight this evaluation challenge, we propose the FECE dataset for evaluating fine-grained correction quality. Experiments on FECE show that existing metrics do not always reflect fine-grained correction performance. To enable the evaluation of fine-grained correction performance, we further propose COMET_Fine, a fine-grained evaluation metric. COMET_Fine can effectively assess fine-grained correction quality while also revealing inconsistencies between existing metrics and COMET_Fine.</abstract>
    <identifier type="citekey">ding-duan-2026-fece</identifier>
    <location>
        <url>https://aclanthology.org/2026.wmt-main.95/</url>
    </location>
    <part>
        <date>2026-10</date>
        <extent unit="page">
            <start>1624</start>
            <end>1637</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T FECE: A Challenge Set for Evaluating Fine-Grained Translation Error Correction
%A Ding, Jie
%A Duan, Xiangyu
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Eleventh Conference on Machine Translation
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-474-3
%F ding-duan-2026-fece
%X Existing machine translation evaluation metrics assess translation quality only at the segment level and therefore cannot assess fine-grained translation quality. For translation error correction systems, this means that the quality of fine-grained corrections cannot be evaluated using existing metrics. To highlight this evaluation challenge, we propose the FECE dataset for evaluating fine-grained correction quality. Experiments on FECE show that existing metrics do not always reflect fine-grained correction performance. To enable the evaluation of fine-grained correction performance, we further propose COMET_Fine, a fine-grained evaluation metric. COMET_Fine can effectively assess fine-grained correction quality while also revealing inconsistencies between existing metrics and COMET_Fine.
%U https://aclanthology.org/2026.wmt-main.95/
%P 1624-1637

Download as File

Markdown (Informal)

[FECE: A Challenge Set for Evaluating Fine-Grained Translation Error Correction](https://aclanthology.org/2026.wmt-main.95/) (Ding & Duan, WMT 2026)

FECE: A Challenge Set for Evaluating Fine-Grained Translation Error Correction (Ding & Duan, WMT 2026)

ACL

Jie Ding and Xiangyu Duan. 2026. FECE: A Challenge Set for Evaluating Fine-Grained Translation Error Correction. In Proceedings of the Eleventh Conference on Machine Translation, pages 1624–1637, Budapest, Hungary. Association for Computational Linguistics.