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

• URL: http://arxiv.org/abs/2510.24707v1
• Date: Tue, 28 Oct 2025 17:56:20 GMT
• Title: MetricX-25 and GemSpanEval: Google Translate Submissions to the WMT25 Evaluation Shared Task
• Authors: Juraj Juraska, Tobias Domhan, Mara Finkelstein, Tetsuji Nakagawa, Geza Kovacs, Daniel Deutsch, Pidong Wang, Markus Freitag,
• Abstract summary: We present our submissions to the unified WMT25 Translation Evaluation Shared Task.<n>For the Quality Score Prediction subtask, we create a new generation of MetricX with improvements in the input format and the training protocol.<n>For the Error Span Detection subtask, we develop a new model, GemSpanEval, trained to predict error spans along with their severities and categories.
• Score: 20.03717974553634
• License: http://creativecommons.org/licenses/by/4.0/
• 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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