One of the significant challenges of Machine Translation (MT) is the scarcity
of large amounts of data, mainly parallel sentence aligned corpora. If the
evaluation is as rigorous as resource-rich languages, both Neural Machine
Translation (NMT) and Statistical Machine Translation (SMT) can produce good
results with such large amounts of data. However, it is challenging to improve
the quality of MT output for low resource languages, especially in NMT and SMT.
In order to tackle the challenges faced by MT, we present a novel approach of
using a scaled similarity score of sentences, especially for related languages
based on a 5-gram KenLM language model with Kneser-ney smoothing technique for
filtering in-domain data from out-of-domain corpora that boost the translation
quality of MT. Furthermore, we employ other domain adaptation techniques such
as multi-domain, fine-tuning and iterative back-translation approach to compare
our novel approach on the Hindi-Nepali language pair for NMT and SMT. Our
approach succeeds in increasing ~2 BLEU point on multi-domain approach, ~3 BLEU
point on fine-tuning for NMT and ~2 BLEU point on iterative back-translation
approach.

本文提出了一种新颖的方法使用基于 5-gram KenLM 语言模型的缩放相似性分数，尤其是对于相关语言，该方法使用 Kneser-ney 平滑技术从域内数据中过滤出域外数据，以提高机器翻译的翻译质量。 此外，我们采用了其他域自适应技术，如多域、微调和迭代回译方法，以比较我们在 Hindi-Nepali 语言对上的新方法在 NMT 和 SMT 上的效果，我们的方法在多域方法上增加了约 2 个 BLEU 点，在微调 NMT 方面提高了约 3 个 BLEU 点，在迭代回译上提高了约 2 个 BLEU 点。