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arXiv · 2510.24707

MetricX-25 and GemSpanEval: Google Translate Submissions to the WMT25 Evaluation Shared Task

Juraj Juraska·Tobias Domhan·Mara Finkelstein·Tetsuji Nakagawa·Geza Kovacs·Daniel Deutsch·Pidong Wang·Markus Freitag
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Abstract

In this paper, we present our submissions to the unified WMT25 Translation Evaluation Shared Task. For the Quality Score Prediction subtask, we create a new generation of MetricX with improvements in the input format and the training protocol, while for the Error Span Detection subtask we develop a new model, GemSpanEval, trained to predict error spans along with their severities and categories. Both systems are based on the state-of-the-art multilingual open-weights model Gemma 3, fine-tuned on publicly available WMT data. We demonstrate that MetricX-25, adapting Gemma 3 to an encoder-only architecture with a regression head on top, can be trained to effectively predict both MQM and ESA quality scores, and significantly outperforms its predecessor. Our decoder-only GemSpanEval model, on the other hand, we show to be competitive in error span detection with xCOMET, a strong encoder-only sequence-tagging baseline. With error span detection formulated as a generative task, we instruct the model to also output the context for each predicted error span, thus ensuring that error spans are identified unambiguously.

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Juraj Juraska, Tobias Domhan, Mara Finkelstein, Tetsuji Nakagawa, Geza Kovacs, Daniel Deutsch, Pidong Wang, Markus Freitag. 2025-10-28. MetricX-25 and GemSpanEval: Google Translate Submissions to the WMT25 Evaluation Shared Task. https://arxiv.org/abs/2510.24707

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