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        <title>Lexilogic@IWSLT 2026: Pairwise Ranking Fine-tuning of CometKiwi for Speech Translation Quality Estimation</title>
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        <namePart type="given">Pranav</namePart>
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            <title>Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026)</title>
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        <name type="personal">
            <namePart type="given">Elizabeth</namePart>
            <namePart type="family">Salesky</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Antonios</namePart>
            <namePart type="family">Anastasopoulos</namePart>
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                <roleTerm authority="marcrelator" type="text">editor</roleTerm>
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        <name type="personal">
            <namePart type="given">Matteo</namePart>
            <namePart type="family">Negri</namePart>
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        <name type="personal">
            <namePart type="given">Marcello</namePart>
            <namePart type="family">Federico</namePart>
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    <abstract>We describe our submission to the IWSLT 2026 Speech Translation Metrics Shared Task for the ASR text to translated text evaluation scenario. We fine-tune CometKiwi-22, a 580M-parameter quality estimation model, with a pair-wise ranking objective, and construct within-document translation pairs and train with an adaptive margin ranking loss combined with mean squared error (MSE) calibration. Our system achieves 35.2% per-source Kendall’s \ensuremathτ on the dev (development) set.</abstract>
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%0 Conference Proceedings
%T Lexilogic@IWSLT 2026: Pairwise Ranking Fine-tuning of CometKiwi for Speech Translation Quality Estimation
%A Gupta, Pranav
%Y Salesky, Elizabeth
%Y Anastasopoulos, Antonios
%Y Negri, Matteo
%Y Federico, Marcello
%S Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, USA (in-person and online)
%@ 979-8-89176-411-8
%F gupta-2026-lexilogic
%X We describe our submission to the IWSLT 2026 Speech Translation Metrics Shared Task for the ASR text to translated text evaluation scenario. We fine-tune CometKiwi-22, a 580M-parameter quality estimation model, with a pair-wise ranking objective, and construct within-document translation pairs and train with an adaptive margin ranking loss combined with mean squared error (MSE) calibration. Our system achieves 35.2% per-source Kendall’s \ensuremathτ on the dev (development) set.
%U https://aclanthology.org/2026.iwslt-1.38/
%P 332-335

Markdown (Informal)

[Lexilogic@IWSLT 2026: Pairwise Ranking Fine-tuning of CometKiwi for Speech Translation Quality Estimation](https://aclanthology.org/2026.iwslt-1.38/) (Gupta, IWSLT 2026)

• Lexilogic@IWSLT 2026: Pairwise Ranking Fine-tuning of CometKiwi for Speech Translation Quality Estimation (Gupta, IWSLT 2026)

ACL

• Pranav Gupta. 2026. Lexilogic@IWSLT 2026: Pairwise Ranking Fine-tuning of CometKiwi for Speech Translation Quality Estimation. In Proceedings of the 23rd International Conference on Spoken Language Translation (IWSLT 2026), pages 332–335, San Diego, USA (in-person and online). Association for Computational Linguistics.