TTLab 在 AlexandriaX-2026 上用 MARBERTv2 做阿拉伯语机翻错误跨度检测
TTLab at AlexandriaX-2026: A Fine-Tuned Surface Tagger for Arabic Machine-Translation Error-Span Detection and Classification
TTLab 团队用 MARBERTv2 检测阿拉伯语机翻的错误片段，加了焦点损失和方言阈值，六款编码器里效果最好，在赛事拿了第三名。
TTLab 团队参加 AlexandriaX-2026 共享任务 Subtask 3，将阿拉伯语机器翻译错误跨度检测与分类建模为词元级分类任务。系统采用焦点损失加类别权重应对严重的标签不平衡，并针对不同方言设置独立解码阈值。在六个阿拉伯语预训练编码器中，MARBERTv2 表现最佳，在开发集和测试集分别取得 40.8 和 40.91 的成绩，在所有参赛队伍中排名第三。团队指出稀有错误类型的分类仍是短板，需要对尾部类别做数据增强。代码已在 GitHub 开源。
TTLab at AlexandriaX-2026: A Fine-Tuned Surface Tagger for Arabic Machine-Translation Error-Span Detection and Classification
We present TTLab's submission to the AlexandriaX-2026 Subtask~3 on Arabic MT error span detection and classification. Our system frames the task as token-level classification over surface forms, preserving character offsets to ensure exact alignment with the evaluation metric. To handle severe label imbalance, we employ a focal loss with class weighting and dialect-specific decoding thresholds. Among six Arabic pre-trained encoders, MARBERTv2 achieves the best overall performance of 40.8 and 40.91 on the development and test set, respectively, ranking $\nth{3}$ out of all participating teams. While our system localizes error spans effectively, classification of rare error types remains challenging, highlighting the need for data augmentation for tail categories. The code is available at ${\href{https://github.com/ENTAILab/arabic-dialectal-mt-error-span-detection}{\faGithub~ TTLab at AlexandriaX-2026}$
