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        <title>A Compliance-Preserving Retrieval System for Aircraft MRO Task Search</title>
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        <namePart type="given">Byungho</namePart>
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            <title>Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)</title>
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        <name type="personal">
            <namePart type="given">Yevgen</namePart>
            <namePart type="family">Matusevych</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Gülşen</namePart>
            <namePart type="family">Eryiğit</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Nikolaos</namePart>
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    <abstract>Aircraft Maintenance Technicians (AMTs) spend up to 30% of work time searching manuals—a documented efficiency bottleneck in MRO operations where every procedure must be traceable to certified sources.We present a compliance-preserving retrieval system that adapts LLM reranking and semantic search to aviation MRO environments by operating alongside, rather than replacing, certified legacy viewers.The system constructs revision-robust embeddings from ATA chapter hierarchies and uses vision-language parsing to structure certified content, allowing technicians to preview ranked tasks and access verified procedures in existing viewers.Evaluation on 49k synthetic queries achieves \ensuremath&gt;90% retrieval accuracy, while bilingual controlled studies with 10 licensed AMTs demonstrate 90.9% top-10 success rate and 95% reduction in lookup time—from 6-15 minutes to 18 seconds per task.These gains provide concrete evidence that semantic retrieval can operate within strict regulatory constraints and meaningfully reduce operational workload in real-world multilingual MRO workflows.</abstract>
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        <url>https://aclanthology.org/2026.eacl-industry.23/</url>
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    <part>
        <date>2026-03</date>
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            <start>317</start>
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%0 Conference Proceedings
%T A Compliance-Preserving Retrieval System for Aircraft MRO Task Search
%A Jo, Byungho
%Y Matusevych, Yevgen
%Y Eryiğit, Gülşen
%Y Aletras, Nikolaos
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-384-5
%F jo-2026-compliance
%X Aircraft Maintenance Technicians (AMTs) spend up to 30% of work time searching manuals—a documented efficiency bottleneck in MRO operations where every procedure must be traceable to certified sources.We present a compliance-preserving retrieval system that adapts LLM reranking and semantic search to aviation MRO environments by operating alongside, rather than replacing, certified legacy viewers.The system constructs revision-robust embeddings from ATA chapter hierarchies and uses vision-language parsing to structure certified content, allowing technicians to preview ranked tasks and access verified procedures in existing viewers.Evaluation on 49k synthetic queries achieves \ensuremath>90% retrieval accuracy, while bilingual controlled studies with 10 licensed AMTs demonstrate 90.9% top-10 success rate and 95% reduction in lookup time—from 6-15 minutes to 18 seconds per task.These gains provide concrete evidence that semantic retrieval can operate within strict regulatory constraints and meaningfully reduce operational workload in real-world multilingual MRO workflows.
%U https://aclanthology.org/2026.eacl-industry.23/
%P 317-329

Markdown (Informal)

[A Compliance-Preserving Retrieval System for Aircraft MRO Task Search](https://aclanthology.org/2026.eacl-industry.23/) (Jo, EACL 2026)

• A Compliance-Preserving Retrieval System for Aircraft MRO Task Search (Jo, EACL 2026)

ACL

• Byungho Jo. 2026. A Compliance-Preserving Retrieval System for Aircraft MRO Task Search. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track), pages 317–329, Rabat, Morocco. Association for Computational Linguistics.