为俄语文本事实一致性评估引入新工具，填补中文语料空白
Adapting AlignScore Mertic for Factual Consistency Evaluation of Text in Russian: A Student Abstract
- 基于RuBERT微调，将AlignScore适配至俄语
- 在俄语与英译俄语数据集上验证了指标有效性
- 适合从事多语言NLP评估的研究者使用
生成文本的事实一致性对可靠自然语言处理应用至关重要。然而，现有评估工具主要针对英语语料，缺乏对俄语文本的评测能力。为此，我们提出AlignRuScore，即对AlignScore度量的全面俄语适配。通过在俄语及英译俄语数据集上，使用基于RuBERT的对齐模型并添加特定任务分类与回归头进行微调，实现了该度量在俄语中的成功迁移。结果表明，统一的对齐度量可有效应用于俄语，为构建鲁棒的多语言事实一致性评估体系奠定基础。我们公开了翻译语料、模型检查点与代码，以支持后续研究。
原文摘要 · Abstract (English)
Ensuring factual consistency in generated text is crucial for reliable natural language processing applications. However, there is a lack of evaluation tools for factual consistency in Russian texts, as existing tools primarily focus on English corpora. To bridge this gap, we introduce AlignRuScore, a comprehensive adaptation of the AlignScore metric for Russian. To adapt the metric, we fine-tuned a RuBERT-based alignment model with task-specific classification and regression heads on Russian and translated English datasets. Our results demonstrate that a unified alignment metric can be successfully ported to Russian, laying the groundwork for robust multilingual factual consistency evaluation. We release the translated corpora, model checkpoints, and code to support further research.
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