Hallucinations in machine translation are translations that contain information completely unrelated to the input. Omissions are translations that do not include some of the input information. While both cases tend to be catastrophic errors undermining user trust, annotated data with these types of pathologies is extremely scarce and is limited to a few high-resource languages. In this work, we release an annotated dataset for the hallucination and omission phenomena covering 18 translation directions with varying resource levels and scripts. Our annotation covers different levels of partial and full hallucinations as well as omissions both at the sentence and at the word level. Additionally, we revisit previous methods for hallucination and omission detection, show that conclusions made based on a single language pair largely do not hold for a large-scale evaluation, and establish new solid baselines.

翻译：机器翻译中的幻觉是指翻译包含与输入完全无关的信息，而遗漏则指翻译未包含部分输入信息。虽然这两类错误通常都会破坏用户信任，但标注此类病理现象的数据极其稀缺，且仅限于少数高资源语言。在本工作中，我们发布了一个覆盖18个翻译方向（包含不同资源等级与书写系统）的幻觉与遗漏现象人工标注数据集。我们的标注涵盖句子层级和词汇层级的局部及完全幻觉与遗漏现象。此外，我们重新审视了此前用于幻觉与遗漏检测的方法，证明基于单一语言对得出的结论在大规模评估中基本不成立，并建立了新的可靠基线。