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

We present Joey NMT, a minimalist neural machine translation toolkit based on
PyTorch that is specifically designed for novices. Joey NMT provides many
popular NMT features in a small and simple code base, so that novices can
easily and quickly learn to use it and adapt it to their needs. Despite its
focus on simplicity, Joey NMT supports classic architectures (RNNs,
transformers), fast beam search, weight tying, and more, and achieves
performance comparable to more complex toolkits on standard benchmarks. We
evaluate the accessibility of our toolkit in a user study where novices with
general knowledge about Pytorch and NMT and experts work through a
self-contained Joey NMT tutorial, showing that novices perform almost as well
as experts in a subsequent code quiz. Joey NMT is available at
this https URL .

Joey NMT 使用 PyTorch，专为新手设计，提供流行的 NMT 功能，支持 RNN 和 Transformer 等经典架构，性能与更复杂的工具包相当，易于学习和适应。