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!

In this study, a human evaluation is carried out on how hyperparameter
settings impact the quality of Transformer-based Neural Machine Translation
(NMT) for the low-resourced English--Irish pair. SentencePiece models using
both Byte Pair Encoding (BPE) and unigram approaches were appraised. Variations
in model architectures included modifying the number of layers, evaluating the
optimal number of heads for attention and testing various regularisation
techniques. The greatest performance improvement was recorded for a
Transformer-optimized model with a 16k BPE subword model. Compared with a
baseline Recurrent Neural Network (RNN) model, a Transformer-optimized model
demonstrated a BLEU score improvement of 7.8 points. When benchmarked against
Google Translate, our translation engines demonstrated significant
improvements. Furthermore, a quantitative fine-grained manual evaluation was
conducted which compared the performance of machine translation systems. Using
the Multidimensional Quality Metrics (MQM) error taxonomy, a human evaluation
of the error types generated by an RNN-based system and a Transformer-based
system was explored. Our findings show the best-performing Transformer system
significantly reduces both accuracy and fluency errors when compared with an
RNN-based model.

该研究评估了超参数设置对基于 Transformer 的神经机器翻译在英语 - 爱尔兰语低资源对中的质量的影响。研究中使用了基于 Byte Pair Encoding (BPE) 和 unigram 方法的 SentencePiece 模型。通过修改层数、评估注意力机制中最佳头数、使用不同的正则化技术等变体，发现使用 16k BPE 子词模型优化的 Transformer 模型表现最佳，相较于基线递归神经网络（RNN）模型，BLEU 分数提高了 7.8 个点，与 Google 翻译相比，该翻译引擎实现了显著提升。此外，还通过定量细粒度手动评估，比较了机器翻译系统的表现。使用多维质量度量中的错误分类法（MQM error taxonomy），探讨了基于 RNN 模型和 Transformer 模型生成的错误类型的人类评估。结果表明，表现最佳的 Transformer 系统在准确性和流畅性错误方面与基于 RNN 的模型相比均有显著降低。

英爱转换器式 NMT 的人工评估

Human Evaluation of English--Irish Transformer-Based NMT

In Transformer-based neural machine translation (NMT), the positional
encoding mechanism helps the self-attention networks to learn the source
representation with order dependency, which makes the Transformer-based NMT
achieve state-of-the-art results for various translation tasks. However,
Transformer-based NMT only adds representations of positions sequentially to
word vectors in the input sentence and does not explicitly consider reordering
information in this sentence. In this paper, we first empirically investigate
the relationship between source reordering information and translation
performance. The empirical findings show that the source input with the target
order learned from the bilingual parallel dataset can substantially improve
translation performance. Thus, we propose a novel reordering method to
explicitly model this reordering information for the Transformer-based NMT. The
empirical results on the WMT14 English-to-German, WAT ASPEC
Japanese-to-English, and WMT17 Chinese-to-English translation tasks show the
effectiveness of the proposed approach.

本文介绍了如何使用双语平行数据集来模拟目标语序，在改进了位置编码机制的基础上，提出了一种新的序列重排序方法来显式地模拟源语句子的重排序信息，同时实验证明，该方法在多种翻译任务中均有效。