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?

Massively multilingual machine translation models allow for the translation
of a large number of languages with a single model, but have limited
performance on low- and very-low-resource translation directions. Pivoting via
high-resource languages remains a strong strategy for low-resource directions,
and in this paper we revisit ways of pivoting through multiple languages.
Previous work has used a simple averaging of probability distributions from
multiple paths, but we find that this performs worse than using a single pivot,
and exacerbates the hallucination problem because the same hallucinations can
be probable across different paths. As an alternative, we propose MaxEns, a
combination strategy that is biased towards the most confident predictions,
hypothesising that confident predictions are less prone to be hallucinations.
We evaluate different strategies on the FLORES benchmark for 20 low-resource
language directions, demonstrating that MaxEns improves translation quality for
low-resource languages while reducing hallucination in translations, compared
to both direct translation and an averaging approach. On average, multi-pivot
strategies still lag behind using English as a single pivot language, raising
the question of how to identify the best pivoting strategy for a given
translation direction.

大规模多语言机器翻译模型在一种模型中翻译大量语言，但在低资源和非常低资源的翻译方向上的性能有限。在这篇论文中，我们重新审视通过多个语言进行枢轴翻译的方法。我们提出了 MaxEns 作为一种组合策略，偏向于最自信的预测，以减少翻译中出现的幻觉问题。通过在 FLORES 基准测试中评估不同策略，我们证明 MaxEns 提高低资源语言的翻译质量，同时减少翻译中的幻觉，相对于直接翻译和平均方法。平均而言，多枢轴策略仍然落后于使用英语作为单个枢轴语言，这引发了如何为特定翻译方向确定最佳枢轴策略的问题。

多枢轴集成和大规模多语言机器翻译模型的研究

Investigating Multi-Pivot Ensembling with Massively Multilingual Machine  Translation Models

It has been shown that the performance of neural machine translation (NMT)
drops starkly in low-resource conditions, often requiring large amounts of
auxiliary data to achieve competitive results. An effective method of
generating auxiliary data is back-translation of target language sentences. In
this work, we present a case study of Tigrinya where we investigate several
back-translation methods to generate synthetic source sentences. We find that
in low-resource conditions, back-translation by pivoting through a
higher-resource language related to the target language proves most effective
resulting in substantial improvements over baselines.

本文主要研究神经机器翻译在低资源条件下的表现，提出通过对目标语句子进行回译生成人工数据的方法，并以提取 Tigrinya 语言为案例，研究了多种回译方法，最终发现及时利用中间语言进行回译是在低资源条件下提高性能的最有效方法。