Mar 2026· arXiv.org· Vol abs/2603.12983· 0 citations· 46 references
Computer Science
TL;DR
A novel self-evolution framework based on Minimum Bayes Risk (MBR) decoding is proposed, named Iterative MBR Distillation for ESD, which eliminates the reliance on human annotations by leveraging an off-the-shelf LLM to generate pseudo-labels.
Abstract
Error Span Detection (ESD) is a crucial subtask in Machine Translation (MT) evaluation, aiming to identify the location and severity of translation errors. While fine-tuning models on human-annotated data improves ESD performance, acquiring such data is expensive and prone to inconsistencies among annotators. To address this, we propose a novel self-evolution framework based on Minimum Bayes Risk (MBR) decoding, named Iterative MBR Distillation for ESD, which eliminates the reliance on human annotations by leveraging an off-the-shelf LLM to generate pseudo-labels. Extensive experiments on the WMT Metrics Shared Task datasets demonstrate that models trained solely on these self-generated pseudo-labels outperform both unadapted base model and supervised baselines trained on human annotations at the system and span levels, while maintaining competitive sentence-level performance.
Desired-Update-Aligned Synthetic Data (DASA), which uses activation-gradient feedback from a frozen reference model to guide the optimization of continuous synthetic input embeddings, inspired by the role of activation gradients in local risk reduction, targets useful adaptation updates rather than source-text reconstr...
Jin-Hao Zhang, Ze-Yu Liu, Zi-Cheng Yan et al.· 0 citations
FACET, the authors' reference-free submission to the WMT26 Automated Translation Quality Evaluation Task, is presented, which decomposes evaluation into Fluency, Accuracy, and Consistency passes and gives each pass only the context its error type requires.
Ahrii Kim, Chanjun Park, Seong-heum Kim· 0 citations
A black-box, inference-time diagnostic that tells these two cases apart without retraining or annotation is introduced, with the pattern holding on English-Marathi and English-Tamil, with the failure modes tracking the post-edit distribution rather than MT quality or language family.
Isuru Wijesiri, Nisansa de Silva, Kavindu Warnakulasuriya et al.· 0 citations
This work proposes augmenting existing benchmarks to increase translation difficulty by combining adversarial optimization with a differentiable translation difficulty estimator, and uses gradients from a combined difficulty and fluency objective to iteratively replace tokens in Adversarial Translation Optimization (AT...
William Kalikman, Šimon Sukup, Michal Tesnar et al.· European Association for Mac...· 2 citations
This work proposes a scalable reinforcement-learning-based approach for rewriting existing source texts into instances that are more difficult to translate for MT systems, using a large language model with Group Relative Policy Optimization (GRPO) and fine-tune a large language model with Group Relative Policy Optimiza...
Florian Zogaj, Jakob Hütteneder, Giovanni De Muri et al.· 1 citation
A regression-guided routing approach that prioritizes segments by predicted CER improvement, paired with a safeguard layer that detects harmful LLM corrections and routes uncertain segments to human review, and substantially outperforms standard confidence-based approaches is introduced.
Stergios Konstantinidis, Hayman Lotfy, Michalis Vlachos· Proceedings of the 2026 ACM...· 0 citations
