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    <abstract>We release MTQE.en-he: to our knowledge,the first publicly available English-Hebrewbenchmark for Machine Translation QualityEstimation. MTQE.en-he contains 959 English segments from WMT24++, each pairedwith a machine translation into Hebrew, andDirect Assessment scores of the translationquality annotated by three human experts. Webenchmark ChatGPT prompting, TransQuest,and CometKiwi and show that ensemblingthe three models outperforms the best singlemodel (CometKiwi) by 6.4 percentage pointsPearson and 5.8 percentage points Spearman.Fine-tuning experiments with TransQuest andCometKiwi reveal that full-model updates aresensitive to overfitting and distribution collapse,yet parameter-efficient methods (LoRA, BitFit, and FTHead, i.e., fine-tuning only the classification head)train stably and yield improvements of 2-3 percentage points. MTQE.en-heand our experimental results enable future research on this under-resourced language pair.</abstract>
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%0 Conference Proceedings
%T MTQE.en-he: Machine Translation Quality Estimation for English-Hebrew
%A Rosenbaum, Andy
%A Siani, Assaf
%A Kernerman, Ilan
%Y Hettiarachchi, Hansi
%Y Ranasinghe, Tharindu
%Y Plum, Alistair
%Y Rayson, Paul
%Y Mitkov, Ruslan
%Y Gaber, Mohamed
%Y Premasiri, Damith
%Y Tan, Fiona Anting
%Y Uyangodage, Lasitha
%S Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-377-7
%F rosenbaum-etal-2026-mtqe
%X We release MTQE.en-he: to our knowledge,the first publicly available English-Hebrewbenchmark for Machine Translation QualityEstimation. MTQE.en-he contains 959 English segments from WMT24++, each pairedwith a machine translation into Hebrew, andDirect Assessment scores of the translationquality annotated by three human experts. Webenchmark ChatGPT prompting, TransQuest,and CometKiwi and show that ensemblingthe three models outperforms the best singlemodel (CometKiwi) by 6.4 percentage pointsPearson and 5.8 percentage points Spearman.Fine-tuning experiments with TransQuest andCometKiwi reveal that full-model updates aresensitive to overfitting and distribution collapse,yet parameter-efficient methods (LoRA, BitFit, and FTHead, i.e., fine-tuning only the classification head)train stably and yield improvements of 2-3 percentage points. MTQE.en-heand our experimental results enable future research on this under-resourced language pair.
%U https://aclanthology.org/2026.loreslm-1.48/
%P 559-569

Markdown (Informal)

[MTQE.en-he: Machine Translation Quality Estimation for English-Hebrew](https://aclanthology.org/2026.loreslm-1.48/) (Rosenbaum et al., LoResLM 2026)

• MTQE.en-he: Machine Translation Quality Estimation for English-Hebrew (Rosenbaum et al., LoResLM 2026)

ACL

• Andy Rosenbaum, Assaf Siani, and Ilan Kernerman. 2026. MTQE.en-he: Machine Translation Quality Estimation for English-Hebrew. In Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026), pages 559–569, Rabat, Morocco. Association for Computational Linguistics.