@inproceedings{nwe-etal-2023-enhancing,
title = "Enhancing Translation of {M}yanmar Sign Language by Transfer Learning and Self-Training",
author = "Nwe, Hlaing Myat and
Shirai, Kiyoaki and
Kertkeidkachorn, Natthawut and
Theeramunkong, Thanaruk and
Thu, Ye Kyaw and
Supnithi, Thepchai and
Kaothanthong, Natsuda",
editor = "Utiyama, Masao and
Wang, Rui",
booktitle = "Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track",
month = sep,
year = "2023",
address = "Macau SAR, China",
publisher = "Asia-Pacific Association for Machine Translation",
url = "https://aclanthology.org/2023.mtsummit-research.10",
pages = "111--122",
abstract = "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.",
}
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<abstract>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.</abstract>
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%0 Conference Proceedings
%T Enhancing Translation of Myanmar Sign Language by Transfer Learning and Self-Training
%A Nwe, Hlaing Myat
%A Shirai, Kiyoaki
%A Kertkeidkachorn, Natthawut
%A Theeramunkong, Thanaruk
%A Thu, Ye Kyaw
%A Supnithi, Thepchai
%A Kaothanthong, Natsuda
%Y Utiyama, Masao
%Y Wang, Rui
%S Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track
%D 2023
%8 September
%I Asia-Pacific Association for Machine Translation
%C Macau SAR, China
%F nwe-etal-2023-enhancing
%X 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.
%U https://aclanthology.org/2023.mtsummit-research.10
%P 111-122
Markdown (Informal)
[Enhancing Translation of Myanmar Sign Language by Transfer Learning and Self-Training](https://aclanthology.org/2023.mtsummit-research.10) (Nwe et al., MTSummit 2023)
ACL
- Hlaing Myat Nwe, Kiyoaki Shirai, Natthawut Kertkeidkachorn, Thanaruk Theeramunkong, Ye Kyaw Thu, Thepchai Supnithi, and Natsuda Kaothanthong. 2023. Enhancing Translation of Myanmar Sign Language by Transfer Learning and Self-Training. In Proceedings of Machine Translation Summit XIX, Vol. 1: Research Track, pages 111–122, Macau SAR, China. Asia-Pacific Association for Machine Translation.