@inproceedings{gupta-2026-lexilogic,
    title = "Lexilogic@{IWSLT} 2026: Pairwise Ranking Fine-tuning of {C}omet{K}iwi for Speech Translation Quality Estimation",
    author = "Gupta, Pranav",
    editor = "Salesky, Elizabeth  and
      Anastasopoulos, Antonios  and
      Negri, Matteo  and
      Federico, Marcello",
    booktitle = "Proceedings of the 23rd International Conference on Spoken Language Translation ({IWSLT} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, USA (in-person and online)",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.iwslt-1.38/",
    pages = "332--335",
    ISBN = "979-8-89176-411-8",
    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 {\ensuremath{\tau}} on the dev (development) set."
}
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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)

ACL