论文标题
通过功能归因于神经机器翻译中的幻觉
Reducing Hallucinations in Neural Machine Translation with Feature Attribution
论文作者
论文摘要
神经有条件的语言生成模型实现了神经机器翻译（NMT）的最新技术，但高度依赖于并行培训数据集的质量。当在低质量数据集中接受培训时，这些模型容易出现各种错误类型，包括幻觉，即流利的输出，但与源句子无关。这些错误特别危险，因为从表面上看，翻译可以被视为正确的输出，尤其是在读者不了解源语言的情况下。我们提出了一个案例研究，重点是模型理解和正则化，以减少NMT的幻觉。我们首先使用特征归因方法来研究产生幻觉的NMT模型的行为。然后，我们利用这些方法提出了一种新型损失功能，该功能实质上有助于减少幻觉，并且不需要从头开始重新训练模型。
Neural conditional language generation models achieve the state-of-the-art in Neural Machine Translation (NMT) but are highly dependent on the quality of parallel training dataset. When trained on low-quality datasets, these models are prone to various error types, including hallucinations, i.e. outputs that are fluent, but unrelated to the source sentences. These errors are particularly dangerous, because on the surface the translation can be perceived as a correct output, especially if the reader does not understand the source language. We present a case study focusing on model understanding and regularisation to reduce hallucinations in NMT. We first use feature attribution methods to study the behaviour of an NMT model that produces hallucinations. We then leverage these methods to propose a novel loss function that substantially helps reduce hallucinations and does not require retraining the model from scratch.
