Most current machine translation models are mainly trained with parallel
corpora, and their translation accuracy largely depends on the quality and
quantity of the corpora. Although there are billions of parallel sentences for
a few language pairs, effectively dealing with most language pairs is difficult
due to a lack of publicly available parallel corpora. This paper creates a
large parallel corpus for English-Japanese, a language pair for which only
limited resources are available, compared to such resource-rich languages as
English-German. It introduces a new web-based English-Japanese parallel corpus
named JParaCrawl v3.0. Our new corpus contains more than 21 million unique
parallel sentence pairs, which is more than twice as many as the previous
JParaCrawl v2.0 corpus. Through experiments, we empirically show how our new
corpus boosts the accuracy of machine translation models on various domains.
The JParaCrawl v3.0 corpus will eventually be publicly available online for
research purposes.

本文介绍了一个新的基于网络的英日平行语料库 JParaCrawl v3.0，其包括超过 2100 万个独特的平行句对，证明它可以提高各种领域机器翻译模型的准确性，并将在将来公开发布。

JParaCrawl v3.0：大规模英日平行语料库

JParaCrawl v3.0: A Large-scale English-Japanese Parallel Corpus

Recent machine translation algorithms mainly rely on parallel corpora.
However, since the availability of parallel corpora remains limited, only some
resource-rich language pairs can benefit from them. We constructed a parallel
corpus for English-Japanese, for which the amount of publicly available
parallel corpora is still limited. We constructed the parallel corpus by
broadly crawling the web and automatically aligning parallel sentences. Our
collected corpus, called JParaCrawl, amassed over 8.7 million sentence pairs.
We show how it includes a broader range of domains and how a neural machine
translation model trained with it works as a good pre-trained model for
fine-tuning specific domains. The pre-training and fine-tuning approaches
achieved or surpassed performance comparable to model training from the initial
state and reduced the training time. Additionally, we trained the model with an
in-domain dataset and JParaCrawl to show how we achieved the best performance
with them. JParaCrawl and the pre-trained models are freely available online
for research purposes.

本文介绍了构建并通过相关实验验证了英日平行语料库 JParaCrawl 的优越性以及使用其进行预训练和微调的方法，该方法在特定领域上可达到实例训练模型的水平并缩短训练时间。

JParaCrawl：大规模基于 Web 的英日平行语料库

JParaCrawl: A Large Scale Web-Based English-Japanese Parallel Corpus

Cross-lingual word embeddings (CLWE) underlie many multilingual natural
language processing systems, often through orthogonal transformations of
pre-trained monolingual embeddings. However, orthogonal mapping only works on
language pairs whose embeddings are naturally isomorphic. For non-isomorphic
pairs, our method (Iterative Normalization) transforms monolingual embeddings
to make orthogonal alignment easier by simultaneously enforcing that (1)
individual word vectors are unit length, and (2) each language's average vector
is zero. Iterative Normalization consistently improves word translation
accuracy of three CLWE methods, with the largest improvement observed on
English-Japanese (from 2% to 44% test accuracy).

通过迭代正则化方法，使得非同构语言 pairs 的单语词嵌入向量单位化和对齐，从而提高跨语言词嵌入的翻译准确度，特别是在英文 - 日文 pairs 中，测试准确性从 2% 提高到 44%。