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.
Related papers
- Revisiting the Scaling Properties of Downstream Metrics in Large Language Model Training [11.179110411255708]
We propose a direct framework to model the scaling of benchmark performance from the training budget.<n>Our results show that the direct approach extrapolates better than the previously proposed two-stage procedure.<n>We release the complete set of pretraining losses and downstream evaluation results.
arXiv Detail & Related papers (2025-12-09T18:33:48Z) - Detect Anything via Next Point Prediction [51.55967987350882]
Rex- Omni is a 3B-scale MLLM that achieves state-of-the-art object perception performance.<n>On benchmarks like COCO and LVIS, Rex- Omni attains performance comparable to or exceeding regression-based models.
arXiv Detail & Related papers (2025-10-14T17:59:54Z) - NeKo: Toward Post Recognition Generative Correction Large Language Models with Task-Oriented Experts [57.53692236201343]
We propose a Multi-Task Correction MoE, where we train the experts to become an expert'' of speech-to-text, language-to-text and vision-to-text datasets.
NeKo performs competitively on grammar and post-OCR correction as a multi-task model.
arXiv Detail & Related papers (2024-11-08T20:11:24Z) - Task-customized Masked AutoEncoder via Mixture of Cluster-conditional
Experts [104.9871176044644]
Masked Autoencoder(MAE) is a prevailing self-supervised learning method that achieves promising results in model pre-training.
We propose a novel MAE-based pre-training paradigm, Mixture of Cluster-conditional Experts (MoCE)
MoCE trains each expert only with semantically relevant images by using cluster-conditional gates.
arXiv Detail & Related papers (2024-02-08T03:46:32Z) - Unify word-level and span-level tasks: NJUNLP's Participation for the
WMT2023 Quality Estimation Shared Task [59.46906545506715]
We introduce the NJUNLP team to the WMT 2023 Quality Estimation (QE) shared task.
Our team submitted predictions for the English-German language pair on all two sub-tasks.
Our models achieved the best results in English-German for both word-level and fine-grained error span detection sub-tasks.
arXiv Detail & Related papers (2023-09-23T01:52:14Z) - Annotating and Detecting Fine-grained Factual Errors for Dialogue
Summarization [34.85353544844499]
We present the first dataset with fine-grained factual error annotations named DIASUMFACT.
We define fine-grained factual error detection as a sentence-level multi-label classification problem.
We propose an unsupervised model ENDERANKER via candidate ranking using pretrained encoder-decoder models.
arXiv Detail & Related papers (2023-05-26T00:18:33Z) - Towards Fine-Grained Information: Identifying the Type and Location of
Translation Errors [80.22825549235556]
Existing approaches can not synchronously consider error position and type.
We build an FG-TED model to predict the textbf addition and textbfomission errors.
Experiments show that our model can identify both error type and position concurrently, and gives state-of-the-art results.
arXiv Detail & Related papers (2023-02-17T16:20:33Z) - uChecker: Masked Pretrained Language Models as Unsupervised Chinese
Spelling Checkers [23.343006562849126]
We propose a framework named textbfuChecker to conduct unsupervised spelling error detection and correction.
Masked pretrained language models such as BERT are introduced as the backbone model.
Benefiting from the various and flexible MASKing operations, we propose a Confusionset-guided masking strategy to fine-train the masked language model.
arXiv Detail & Related papers (2022-09-15T05:57:12Z) - Tail-to-Tail Non-Autoregressive Sequence Prediction for Chinese
Grammatical Error Correction [49.25830718574892]
We present a new framework named Tail-to-Tail (textbfTtT) non-autoregressive sequence prediction.
Considering that most tokens are correct and can be conveyed directly from source to target, and the error positions can be estimated and corrected.
Experimental results on standard datasets, especially on the variable-length datasets, demonstrate the effectiveness of TtT in terms of sentence-level Accuracy, Precision, Recall, and F1-Measure.
arXiv Detail & Related papers (2021-06-03T05:56:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.