利用蕴含判断改进跨语言摘要
Leveraging Entailment Judgements in Cross-Lingual Summarisation
AI总结：
本文针对跨语言摘要数据中的幻觉问题，提出用跨语言自然语言推理评估忠实度，并通过非似然损失训练模型，在保持信息量的同时提升摘要忠实度。
摘要：
人工合成的跨语言摘要（CLS）数据集容易包含文档-摘要对，其中参考摘要对相应文档不忠实，因为它包含文档不支持的内容（即幻觉内容）。这种低数据质量会误导模型学习并掩盖评估结果。针对单语摘要（主要以英语为主），已经提出了自动评估幻觉和改善训练的方法。对于跨语言摘要，我们提出使用现成的跨语言自然语言推理（X-NLI）来评估参考摘要和模型生成摘要的忠实度。然后，我们研究了能够感知训练数据中忠实度问题的训练方法，并提出一种使用非似然损失来教会模型识别不忠实摘要序列的方法。我们的结果表明，可以训练出生成更忠实摘要的跨语言摘要模型，同时保持可比或更好的信息量。
英文摘要：
Synthetically created Cross-Lingual Summarisation (CLS) datasets are prone to include document-summary pairs where the reference summary is unfaithful to the corresponding document as it contains content not supported by the document (i.e., hallucinated content). This low data quality misleads model learning and obscures evaluation results. Automatic ways to assess hallucinations and improve training have been proposed for monolingual summarisation, predominantly in English. For CLS, we propose to use off-the-shelf cross-lingual Natural Language Inference (X-NLI) to evaluate faithfulness of reference and model generated summaries. Then, we study training approaches that are aware of faithfulness issues in the training data and propose an approach that uses unlikelihood loss to teach a model about unfaithful summary sequences. Our results show that it is possible to train CLS models that yield more faithful summaries while maintaining comparable or better informativess.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
