The conversion of content from one language to another utilizing a computer
system is known as Machine Translation (MT). Various techniques have come up to
ensure effective translations that retain the contextual and lexical
interpretation of the source language. End-to-end Neural Machine Translation
(NMT) is a popular technique and it is now widely used in real-world MT
systems. Massive amounts of parallel datasets (sentences in one language
alongside translations in another) are required for MT systems. These datasets
are crucial for an MT system to learn linguistic structures and patterns of
both languages during the training phase. One such dataset is Samanantar, the
largest publicly accessible parallel dataset for Indian languages (ILs). Since
the corpus has been gathered from various sources, it contains many incorrect
translations. Hence, the MT systems built using this dataset cannot perform to
their usual potential. In this paper, we propose an algorithm to remove
mistranslations from the training corpus and evaluate its performance and
efficiency. Two Indic languages (ILs), namely, Hindi (HIN) and Odia (ODI) are
chosen for the experiment. A baseline NMT system is built for these two ILs,
and the effect of different dataset sizes is also investigated. The quality of
the translations in the experiment is evaluated using standard metrics such as
BLEU, METEOR, and RIBES. From the results, it is observed that removing the
incorrect translation from the dataset makes the translation quality better. It
is also noticed that, despite the fact that the ILs-English and English-ILs
systems are trained using the same corpus, ILs-English works more effectively
across all the evaluation metrics.

使用最大的公开可访问的印度语言平行数据集 Samanantar 进行实验，在二种印度语言 Hindi 和 Odia 上构建一个基准神经机器翻译系统，并通过消除数据集中的错误翻译来提高翻译质量，进而发现尽管 ILs-English 和 English-ILs 系统使用相同的数据集进行训练，但 ILs-English 在所有评估指标上的表现更好。