推进自动后编辑的半监督学习：基于错误词掩码填充的数据合成
Advancing Semi-Supervised Learning for Automatic Post-Editing: Data-Synthesis by Mask-Infilling with Erroneous Terms
AI总结：
针对自动后编辑（APE）训练数据缺乏问题，提出基于错误词掩码填充的数据合成方法与选择性语料交织策略，生成模仿真实翻译错误的合成数据，显著提升了APE性能。
摘要：
利用合成数据进行训练的半监督学习，因训练数据的缺乏，已被广泛用于开发自动后编辑（APE）模型。基于此目标，我们专注于创建高质量合成数据的数据合成方法。鉴于APE将可能包含错误的机器翻译结果作为输入，我们提出了一种数据合成方法，使生成的合成数据能够模仿实际数据中的翻译错误。我们通过改编掩码语言模型方法，引入了一种基于加噪的数据合成方法，通过用错误词元填充被掩码的词元，从干净文本生成噪声文本。此外，我们提出了选择性语料交织方法，通过仅提取有利样本结合两个独立的合成数据集，以进一步提升合成数据的质量。实验结果表明，使用我们方法创建的合成数据，其APE性能显著优于现有方法创建的其他合成数据。
英文摘要：
Semi-supervised learning that leverages synthetic data for training has been widely adopted for developing automatic post-editing (APE) models due to the lack of training data. With this aim, we focus on data-synthesis methods to create high-quality synthetic data. Given that APE takes as input a machine-translation result that might include errors, we present a data-synthesis method by which the resulting synthetic data mimic the translation errors found in actual data. We introduce a noising-based data-synthesis method by adapting the masked language model approach, generating a noisy text from a clean text by infilling masked tokens with erroneous tokens. Moreover, we propose selective corpus interleaving that combines two separate synthetic datasets by taking only the advantageous samples to enhance the quality of the synthetic data further. Experimental results show that using the synthetic data created by our approach results in significantly better APE performance than other synthetic data created by existing methods.
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