Abstract
We propose to achieve explainable neural machine translation (NMT) by changing the output representation to explain itself. We present a novel approach to NMT which generates the target sentence by monotonically walking through the source sentence. Word reordering is modeled by operations which allow setting markers in the target sentence and move a target-side write head between those markers. In contrast to many modern neural models, our system emits explicit word alignment information which is often crucial to practical machine translation as it improves explainability. Our technique can outperform a plain text system in terms of BLEU score under the recent Transformer architecture on Japanese-English and Portuguese-English, and is within 0.5 BLEU difference on Spanish-English.
Abstract (translated)
我们建议通过改变输出表示来解释自身来实现可解释的神经机器翻译(NMT)。我们提出了一种新的NMT方法,它通过单调遍历源句来产生目标句子。通过允许在目标句子中设置标记并在这些标记之间移动目标侧写头的操作来建模单词重新排序。与许多现代神经模型相比,我们的系统发出明确的单词对齐信息,这通常对于实际的机器翻译至关重要,因为它提高了可解释性。根据最近日语 - 英语和葡萄牙语 - 英语的Transformer架构,我们的技术在BLEU分数方面可以胜过纯文本系统,并且在西班牙语 - 英语上的差异在0.5 BLEU之内。
URL
https://arxiv.org/abs/1808.09688