Natsuda Kaothanthong


2023

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Enhancing Translation of Myanmar Sign Language by Transfer Learning and Self-Training
Hlaing Myat Nwe | Kiyoaki Shirai | Natthawut Kertkeidkachorn | Thanaruk Theeramunkong | Ye Kyaw Thu | Thepchai Supnithi | Natsuda Kaothanthong
Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track

This paper proposes a method to develop a machine translation (MT) system from Myanmar Sign Language (MSL) to Myanmar Written Language (MWL) and vice versa for the deaf community. Translation of MSL is a difficult task since only a small amount of a parallel corpus between MSL and MWL is available. To address the challenge for MT of the low-resource language, transfer learning is applied. An MT model is trained first for a high-resource language pair, American Sign Language (ASL) and English, then it is used as an initial model to train an MT model between MSL and MWL. The mT5 model is used as a base MT model in this transfer learning. Additionally, a self-training technique is applied to generate synthetic translation pairs of MSL and MWL from a large monolingual MWL corpus. Furthermore, since the segmentation of a sentence is required as preprocessing of MT for the Myanmar language, several segmentation schemes are empirically compared. Results of experiments show that both transfer learning and self-training can enhance the performance of the translation between MSL and MWL compared with a baseline model fine-tuned from a small MSL-MWL parallel corpus only.