While Transformer-based neural machine translation (NMT) is very effective in
high-resource settings, many languages lack the necessary large parallel
corpora to benefit from it. In the context of low-resource (LR) MT between two
closely-related languages, a natural intuition is to seek benefits from
structural "shortcuts", such as copying subwords from the source to the target,
given that such language pairs often share a considerable number of identical
words, cognates, and borrowings. We test Pointer-Generator Networks for this
purpose for six language pairs over a variety of resource ranges, and find weak
improvements for most settings. However, analysis shows that the model does not
show greater improvements for closely-related vs. more distant language pairs,
or for lower resource ranges, and that the models do not exhibit the expected
usage of the mechanism for shared subwords. Our discussion of the reasons for
this behaviour highlights several general challenges for LR NMT, such as modern
tokenization strategies, noisy real-world conditions, and linguistic
complexities. We call for better scrutiny of linguistically motivated
improvements to NMT given the blackbox nature of Transformer models, as well as
for a focus on the above problems in the field.

在低资源机器翻译中，通过 Transformer 网络结构的结构性快捷方式，如从源语言复制子词到目标语言，可以为近缘语言对之间的机器翻译提供微弱的改进，但并不展现与远缘语言对或低资源范围下相似语言对之间更强的改进，并且模型对于共享子词的机制使用并不如预期，这突显了低资源机器翻译领域面临的诸多挑战，如现代分词策略、嘈杂的真实环境和语言复杂性，并呼吁对 Transformer 模型进行更好的语言学动机改进的审视及对该领域上述问题的关注。

低资源机器翻译的指针生成网络：不要复制！

Pointer-Generator Networks for Low-Resource Machine Translation: Don't  Copy That!

The paper describes the University of Cape Town's submission to the
constrained track of the WMT22 Shared Task: Large-Scale Machine Translation
Evaluation for African Languages. Our system is a single multilingual
translation model that translates between English and 8 South / South East
African Languages, as well as between specific pairs of the African languages.
We used several techniques suited for low-resource machine translation (MT),
including overlap BPE, back-translation, synthetic training data generation,
and adding more translation directions during training. Our results show the
value of these techniques, especially for directions where very little or no
bilingual training data is available.

该论文讲述了开普敦大学提交给 WMT22 大规模机器翻译评估竞赛的受限跟踪的情况。该系统是一个单一的多语种翻译模型，可在英语和 8 种南部 / 东南部非洲语言之间进行翻译，并可在特定的非洲语言对之间进行翻译。该研究采用了多种适用于低资源机器翻译的技术，包括重叠 BPE、回译、合成训练数据生成以及在训练过程中增加更多的翻译方向。结果表明，这些技术的价值尤为突出，特别是对于非常少或没有双语训练数据可用的方向。

南非语地区的多语言机器翻译：开普敦大学 WMT22 系统

University of Cape Town's WMT22 System: Multilingual Machine Translation for Southern African Languages

Current approaches to incorporating terminology constraints in machine
translation (MT) typically assume that the constraint terms are provided in
their correct morphological forms. This limits their application to real-world
scenarios where constraint terms are provided as lemmas. In this paper, we
introduce a modular framework for incorporating lemma constraints in neural MT
(NMT) in which linguistic knowledge and diverse types of NMT models can be
flexibly applied. It is based on a novel cross-lingual inflection module that
inflects the target lemma constraints based on the source context. We explore
linguistically motivated rule-based and data-driven neural-based inflection
modules and design English-German health and English-Lithuanian news test
suites to evaluate them in domain adaptation and low-resource MT settings.
Results show that our rule-based inflection module helps NMT models incorporate
lemma constraints more accurately than a neural module and outperforms the
existing end-to-end approach with lower training costs.

论文提出了一种模块化框架，可以用来在神经机器翻译中灵活地应用语言学知识和不同类型的神经机器翻译模型，特别是设计了用于领域适应和低资源机器翻译设置的英德医疗和英立新闻测试套件，结果表明，我们的基于规则的屈折模块比神经模块更准确地引入了引文限制，并且在低成本的训练下优于现有的端到端方法。