论文标题
在机器翻译中检测和减轻幻觉：单独模型内部工作做得好，句子相似性更好
Detecting and Mitigating Hallucinations in Machine Translation: Model Internal Workings Alone Do Well, Sentence Similarity Even Better
论文作者
论文摘要
尽管长期以来已经认识到神经机器翻译中幻觉的问题，但到目前为止，其缓解的进展很少。实际上，最近事实证明，如果没有人为地鼓励模型来幻觉，以前现有的方法却缺乏，甚至标准序列对数概要性更具信息性。这意味着该模型内部的特征可以提供比我们预期的更多信息，并且在使用外部模型和措施之前，我们首先需要问：如果我们只使用翻译模型本身，我们什么都不使用？我们建议使用一种评估对生成翻译的源贡献百分比的方法。直观地，幻觉是与来源“分离”的翻译，因此可以通过低源贡献来识别它们。此方法将最严重幻觉的检测准确性提高了2倍，并能够在测试时间与以前依赖外部模型的最佳方法相提并论。接下来，如果我们远离内部模型特征并允许外部工具，我们表明，使用跨语义嵌入的句子相似性进一步改善了这些结果。
While the problem of hallucinations in neural machine translation has long been recognized, so far the progress on its alleviation is very little. Indeed, recently it turned out that without artificially encouraging models to hallucinate, previously existing methods fall short and even the standard sequence log-probability is more informative. It means that characteristics internal to the model can give much more information than we expect, and before using external models and measures, we first need to ask: how far can we go if we use nothing but the translation model itself ? We propose to use a method that evaluates the percentage of the source contribution to a generated translation. Intuitively, hallucinations are translations "detached" from the source, hence they can be identified by low source contribution. This method improves detection accuracy for the most severe hallucinations by a factor of 2 and is able to alleviate hallucinations at test time on par with the previous best approach that relies on external models. Next, if we move away from internal model characteristics and allow external tools, we show that using sentence similarity from cross-lingual embeddings further improves these results.
