WikiSplit++: 面向拆分与重述的简易数据精炼方法
WikiSplit++: Easy Data Refinement for Split and Rephrase
AI总结：
针对拆分与重述任务中文本生成方法存在的幻觉与拆分不足问题，本文提出简易数据精炼方法，构建WikiSplit++数据集，在更少训练实例下实现了拆分数量和蕴含率的显著提升。
摘要：
拆分与重述（Split and Rephrase）任务旨在将一个复杂句拆分为多个含义相同的简单句，该任务可提升文本可读性，并增强自然语言处理（NLP）下游任务的性能。然而，尽管采用经大规模数据集微调的编码器-解码器模型的文本到文本生成方法能够改进拆分与重述任务的效果，但该方法仍存在幻觉和拆分不足的问题。为解决这些问题，本文提出了一种简单且有效的数据精炼方法。我们通过移除WikiSplit数据集中复杂句不能蕴含至少一个简单句的实例，并反转参考简单句的顺序，构建了WikiSplit++数据集。实验结果表明，即便训练实例数量更少，使用WikiSplit++训练的模型性能也优于使用WikiSplit训练的模型。具体而言，我们的方法在拆分数量和作为幻觉衡量指标的蕴含率上均取得了显著提升。
英文摘要：
The task of Split and Rephrase, which splits a complex sentence into multiple simple sentences with the same meaning, improves readability and enhances the performance of downstream tasks in natural language processing (NLP). However, while Split and Rephrase can be improved using a text-to-text generation approach that applies encoder-decoder models fine-tuned with a large-scale dataset, it still suffers from hallucinations and under-splitting. To address these issues, this paper presents a simple and strong data refinement approach. Here, we create WikiSplit++ by removing instances in WikiSplit where complex sentences do not entail at least one of the simpler sentences and reversing the order of reference simple sentences. Experimental results show that training with WikiSplit++ leads to better performance than training with WikiSplit, even with fewer training instances. In particular, our approach yields significant gains in the number of splits and the entailment ratio, a proxy for measuring hallucinations.
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