标题： 相关性引导的神经机器翻译 显示英文标题

标题： Relevance-guided Neural Machine Translation

摘要： 随着Transformer架构的出现，神经机器翻译（NMT）的结果最近有了显著的提升。然而，在双语和多语言设置中，低资源条件下的结果仍然落后，这是由于可用的单语和/或平行数据数量有限；因此，需要一种高效且可解释的方法来应对数据稀缺的问题。我们提出了一种基于可解释性的NMT训练方法，应用于无监督和有监督的模型训练中，用于三种不同资源水平的语言（法语、古吉拉特语、哈萨克语）与英语之间的翻译。我们的结果表明，该方法具有前景，特别是在低资源条件下进行训练时，优于简单的训练基线；尽管改进幅度较小，但它为该方法和参数的进一步探索以及向其他语言的扩展奠定了基础。 显示英文摘要

摘要： With the advent of the Transformer architecture, Neural Machine Translation (NMT) results have shown great improvement lately. However, results in low-resource conditions still lag behind in both bilingual and multilingual setups, due to the limited amount of available monolingual and/or parallel data; hence, the need for methods addressing data scarcity in an efficient, and explainable way, is eminent. We propose an explainability-based training approach for NMT, applied in Unsupervised and Supervised model training, for translation of three languages of varying resources, French, Gujarati, Kazakh, to and from English. Our results show our method can be promising, particularly when training in low-resource conditions, outperforming simple training baselines; though the improvement is marginal, it sets the ground for further exploration of the approach and the parameters, and its extension to other languages.