The machine translation mechanism translates texts automatically between
different natural languages, and Neural Machine Translation (NMT) has gained
attention for its rational context analysis and fluent translation accuracy.
However, processing low-resource languages that lack relevant training
attributes like supervised data is a current challenge for Natural Language
Processing (NLP). We incorporated a technique known Active Learning with the
NMT toolkit Joey NMT to reach sufficient accuracy and robust predictions of
low-resource language translation. With active learning, a semi-supervised
machine learning strategy, the training algorithm determines which unlabeled
data would be the most beneficial for obtaining labels using selected query
techniques. We implemented two model-driven acquisition functions for selecting
the samples to be validated. This work uses transformer-based NMT systems;
baseline model (BM), fully trained model (FTM) , active learning least
confidence based model (ALLCM), and active learning margin sampling based model
(ALMSM) when translating English to Hindi. The Bilingual Evaluation Understudy
(BLEU) metric has been used to evaluate system results. The BLEU scores of BM,
FTM, ALLCM and ALMSM systems are 16.26, 22.56 , 24.54, and 24.20, respectively.
The findings in this paper demonstrate that active learning techniques helps
the model to converge early and improve the overall quality of the translation
system.

本文使用 Joey NMT 工具包中的 Active Learning 技术，通过两种基于模型的获取函数 (BM, FTM, ALLCM, 和 ALMSM)，研究了英语到印地语的神经机器翻译。本文的结果表明，Active Learning 技术能够使模型更早地达到收敛并提高整个翻译系统的质量，其中 ALLCM 和 ALMSM 两种模型取得了最好的 BLEU 分数。

神经机器翻译的主动学习

Active Learning for Neural Machine Translation

Our research extends the Bilingual Evaluation Understudy (BLEU) evaluation
technique for statistical machine translation to make it more adjustable and
robust. We intend to adapt it to resemble human evaluation more. We perform
experiments to evaluate the performance of our technique against the primary
existing evaluation methods. We describe and show the improvements it makes
over existing methods as well as correlation to them. When human translators
translate a text, they often use synonyms, different word orders or style, and
other similar variations. We propose an SMT evaluation technique that enhances
the BLEU metric to consider variations such as those.

本研究通过在 Bilingual Evaluation Understudy（BLEU）评估技术上进行改进，提出了一种适应人类评估的评估技术，该技术可以考虑包括同义词和词序在内的变化，并且与现有的评估方法相比，有较好的改进和相关性。