Abstract
In this study we investigate the use of Reinforcement Learning (RL) for fine-tuning neural machine translation models for Burmese dialects. We perform experiments using two extremely low-resource Burmese dialect datasets: Burmese-Beik and Burmese-Rakhine employing two different deep learning modeling techniques: bi-LSTM (Seq2Seq) and the Transformer. Our training procedure involved initially training models over varying numbers of epochs: 30 40 50 60 and 70 epochs and then fine-tuning them with RL for additional epochs such that the total number of epochs for each model equaled 100. For instance a model initially trained for 30 epochs was finetuned for an additional 70 epochs using RL. The results show that better quality machine translation was attained with RL over all initial models. Moreover RL yields a significant improvement of Bilingual Evaluation Understudy (BLEU) scores (+4.73 BLEU for Burmese-to-Beik translation with Seq2Seq RL +2.58 for Rakhine-to-Burmese translation with Transformer RL) over some of the baselines not utilizing RL training. ? 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.
