We introduce KazParC, a parallel corpus designed for machine translation
across Kazakh, English, Russian, and Turkish. The first and largest publicly
available corpus of its kind, KazParC contains a collection of 371,902 parallel
sentences covering different domains and developed with the assistance of human
translators. Our research efforts also extend to the development of a neural
machine translation model nicknamed Tilmash. Remarkably, the performance of
Tilmash is on par with, and in certain instances, surpasses that of industry
giants, such as Google Translate and Yandex Translate, as measured by standard
evaluation metrics, such as BLEU and chrF. Both KazParC and Tilmash are openly
available for download under the Creative Commons Attribution 4.0 International
License (CC BY 4.0) through our GitHub repository.

我们介绍了 KazParC，这是一个用于哈萨克语、英语、俄语和土耳其语机器翻译的并行语料库。KazParC 是首个也是最大的公开可用语料库，包含 371,902 个不同领域的平行句子，是通过人工翻译帮助开发的。我们的研究还延伸到了开发一种名为 Tilmash 的神经机器翻译模型。令人惊讶的是，Tilmash 在某些情况下的性能达到或超过了行业巨头，例如 Google Translate 和 Yandex Translate，由标准评估指标（如 BLEU 和 chrF）衡量。KazParC 和 Tilmash 都可在我们的 GitHub 仓库上以 Creative Commons Attribution 4.0 国际许可证（CC BY 4.0）进行下载。

KazParC: 机器翻译用的哈萨克平行语料库

KazParC: Kazakh Parallel Corpus for Machine Translation

The authors of (Cho et al., 2014a) have shown that the recently introduced
neural network translation systems suffer from a significant drop in
translation quality when translating long sentences, unlike existing
phrase-based translation systems. In this paper, we propose a way to address
this issue by automatically segmenting an input sentence into phrases that can
be easily translated by the neural network translation model. Once each segment
has been independently translated by the neural machine translation model, the
translated clauses are concatenated to form a final translation. Empirical
results show a significant improvement in translation quality for long
sentences.

该研究通过将输入句子自动分割成易于神经机器翻译模型翻译的短语来解决长句翻译时翻译质量下降的问题，并证明了这种方法在长句翻译质量上有显著提升。