Machine Translation (MT) between linguistically dissimilar languages is
challenging, especially due to the scarcity of parallel corpora. Prior works
suggest that pivoting through a high-resource language can help translation
into a related low-resource language. However, existing works tend to discard
the source sentence when pivoting. Taking the case of English to Indian
language MT, this paper explores the 'multi-source translation' approach with
pivoting, using both source and pivot sentences to improve translation. We
conducted extensive experiments with various multi-source techniques for
translating English to Konkani, Manipuri, Sanskrit, and Bodo, using Hindi,
Marathi, and Bengali as pivot languages. We find that multi-source pivoting
yields marginal improvements over the state-of-the-art, contrary to previous
claims, but these improvements can be enhanced with synthetic target language
data. We believe multi-source pivoting is a promising direction for
Low-resource translation.

使用多种多源技术和枢轴翻译，本文研究了从英语到印度语言的机器翻译，发现多源枢轴翻译虽然只有微小改进，但与以前的论断相反，这些改进可以通过合成目标语言数据来增强。我们认为，多源枢轴翻译是一种有前景的低资源翻译方向。

多源枢轴机制对于低资源印度语翻译的效果如何？

How effective is Multi-source pivoting for Translation of Low Resource  Indian Languages?

Multi-source translation is an approach to exploit multiple inputs (e.g. in
two different languages) to increase translation accuracy. In this paper, we
examine approaches for multi-source neural machine translation (NMT) using an
incomplete multilingual corpus in which some translations are missing. In
practice, many multilingual corpora are not complete due to the difficulty to
provide translations in all of the relevant languages (for example, in TED
talks, most English talks only have subtitles for a small portion of the
languages that TED supports). Existing studies on multi-source translation did
not explicitly handle such situations. This study focuses on the use of
incomplete multilingual corpora in multi-encoder NMT and mixture of NMT experts
and examines a very simple implementation where missing source translations are
replaced by a special symbol <NULL>. These methods allow us to use incomplete
corpora both at training time and test time. In experiments with real
incomplete multilingual corpora of TED Talks, the multi-source NMT with the
<NULL> tokens achieved higher translation accuracies measured by BLEU than
those by any one-to-one NMT systems.

本文研究使用不完整的多语言语料库的多源神经机器翻译的方法，并通过使用特殊符号来替换缺失语言的方法实现了训练和测试时的不完整语料库，实验结果表明，使用 <NULL> 符号的多源 NMT 系统 BLEU 值优于单独的 NMT 系统。