ParaCLEAN: Improving Translation Quality through Systematic Parallel Data Cleaning

Audrey Mash; Ella Paulina Bohman; Maite Melero

doi:10.63317/36e3vfurjna4

ParaCLEAN: Improving Translation Quality through Systematic Parallel Data Cleaning

Audrey Mash, Ella Paulina Bohman, Maite Melero

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 external publication .)

Authors concatenated from the text boxes above:

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

Abstract

Parallel corpora often contain significant noise, particularly in low-resource settings where both collected and synthetic data are combined. We present ParaCLEAN, a modular pipeline for cleaning parallel data that integrates embeddings-based filtering, language identification, deduplication, and normalisation. Experiments on Catalan to Japanese translation demonstrate that ParaCLEAN improves data quality and downstream MT performance. Ablation studies highlight the contribution of each step. ParaCLEAN is lightweight, reproducible, and extensible for diverse language pairs.

Anthology ID:: 2026.lrec-1.527
Volume:: Proceedings of the Fifteenth Language Resources and Evaluation Conference
Month:: May
Year:: 2026
Address:: Palma de Mallorca, Spain
Editors:: Stelios Piperidis, Núria Bel, Henk van den Heuvel, Nancy Ide, Simon Krek, Antonio Toral
Venue:: LREC
SIG:
Publisher:: ELRA Language Resource Association
Note:
Pages:: 6630–6640
Language:
External URL:: https://lrec.elra.info/lrec2026-main-527
DOI:: 10.63317/36e3vfurjna4
Bibkey:
Cite (ACL):: Audrey Mash, Ella Paulina Bohman, and Maite Melero. 2026. ParaCLEAN: Improving Translation Quality through Systematic Parallel Data Cleaning. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 6630–6640, Palma de Mallorca, Spain. ELRA Language Resource Association.
Cite (Informal):: ParaCLEAN: Improving Translation Quality through Systematic Parallel Data Cleaning (Mash et al., LREC 2026)
Copy Citation:

External Cite Search Fix data

Export citation

BibTeX
MODS XML
Endnote
Preformatted

@inproceedings{mash-etal-2026-paraclean,
    title = "{P}ara{CLEAN}: Improving Translation Quality through Systematic Parallel Data Cleaning",
    author = "Mash, Audrey  and
      Bohman, Ella Paulina  and
      Melero, Maite",
    editor = "Piperidis, Stelios  and
      Bel, N{\'u}ria  and
      van den Heuvel, Henk  and
      Ide, Nancy  and
      Krek, Simon  and
      Toral, Antonio",
    booktitle = "Proceedings of the Fifteenth Language Resources and Evaluation Conference",
    month = may,
    year = "2026",
    address = "Palma de Mallorca, Spain",
    publisher = "ELRA Language Resource Association",
    url = "https://aclanthology.org/2026.lrec-1.527/",
    doi = "10.63317/36e3vfurjna4",
    pages = "6630--6640",
    abstract = "Parallel corpora often contain significant noise, particularly in low-resource settings where both collected and synthetic data are combined. We present ParaCLEAN, a modular pipeline for cleaning parallel data that integrates embeddings-based filtering, language identification, deduplication, and normalisation. Experiments on Catalan to Japanese translation demonstrate that ParaCLEAN improves data quality and downstream MT performance. Ablation studies highlight the contribution of each step. ParaCLEAN is lightweight, reproducible, and extensible for diverse language pairs."
}

Download as File

<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="mash-etal-2026-paraclean">
    <titleInfo>
        <title>ParaCLEAN: Improving Translation Quality through Systematic Parallel Data Cleaning</title>
    </titleInfo>
    <name type="personal">
        <namePart type="given">Audrey</namePart>
        <namePart type="family">Mash</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Ella</namePart>
        <namePart type="given">Paulina</namePart>
        <namePart type="family">Bohman</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <name type="personal">
        <namePart type="given">Maite</namePart>
        <namePart type="family">Melero</namePart>
        <role>
            <roleTerm authority="marcrelator" type="text">author</roleTerm>
        </role>
    </name>
    <originInfo>
        <dateIssued>2026-05</dateIssued>
    </originInfo>
    <typeOfResource>text</typeOfResource>
    <relatedItem type="host">
        <titleInfo>
            <title>Proceedings of the Fifteenth Language Resources and Evaluation Conference</title>
        </titleInfo>
        <name type="personal">
            <namePart type="given">Stelios</namePart>
            <namePart type="family">Piperidis</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Núria</namePart>
            <namePart type="family">Bel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Henk</namePart>
            <namePart type="family">van den Heuvel</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Nancy</namePart>
            <namePart type="family">Ide</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Simon</namePart>
            <namePart type="family">Krek</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <name type="personal">
            <namePart type="given">Antonio</namePart>
            <namePart type="family">Toral</namePart>
            <role>
                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
            </role>
        </name>
        <originInfo>
            <publisher>ELRA Language Resource Association</publisher>
            <place>
                <placeTerm type="text">Palma de Mallorca, Spain</placeTerm>
            </place>
        </originInfo>
        <genre authority="marcgt">conference publication</genre>
    </relatedItem>
    <abstract>Parallel corpora often contain significant noise, particularly in low-resource settings where both collected and synthetic data are combined. We present ParaCLEAN, a modular pipeline for cleaning parallel data that integrates embeddings-based filtering, language identification, deduplication, and normalisation. Experiments on Catalan to Japanese translation demonstrate that ParaCLEAN improves data quality and downstream MT performance. Ablation studies highlight the contribution of each step. ParaCLEAN is lightweight, reproducible, and extensible for diverse language pairs.</abstract>
    <identifier type="citekey">mash-etal-2026-paraclean</identifier>
    <identifier type="doi">10.63317/36e3vfurjna4</identifier>
    <location>
        <url>https://aclanthology.org/2026.lrec-1.527/</url>
    </location>
    <part>
        <date>2026-05</date>
        <extent unit="page">
            <start>6630</start>
            <end>6640</end>
        </extent>
    </part>
</mods>
</modsCollection>

Download as File

%0 Conference Proceedings
%T ParaCLEAN: Improving Translation Quality through Systematic Parallel Data Cleaning
%A Mash, Audrey
%A Bohman, Ella Paulina
%A Melero, Maite
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F mash-etal-2026-paraclean
%X Parallel corpora often contain significant noise, particularly in low-resource settings where both collected and synthetic data are combined. We present ParaCLEAN, a modular pipeline for cleaning parallel data that integrates embeddings-based filtering, language identification, deduplication, and normalisation. Experiments on Catalan to Japanese translation demonstrate that ParaCLEAN improves data quality and downstream MT performance. Ablation studies highlight the contribution of each step. ParaCLEAN is lightweight, reproducible, and extensible for diverse language pairs.
%R 10.63317/36e3vfurjna4
%U https://aclanthology.org/2026.lrec-1.527/
%U https://doi.org/10.63317/36e3vfurjna4
%P 6630-6640

Download as File

Markdown (Informal)

[ParaCLEAN: Improving Translation Quality through Systematic Parallel Data Cleaning](https://aclanthology.org/2026.lrec-1.527/) (Mash et al., LREC 2026)

ParaCLEAN: Improving Translation Quality through Systematic Parallel Data Cleaning (Mash et al., LREC 2026)

ACL

Audrey Mash, Ella Paulina Bohman, and Maite Melero. 2026. ParaCLEAN: Improving Translation Quality through Systematic Parallel Data Cleaning. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 6630–6640, Palma de Mallorca, Spain. ELRA Language Resource Association.