NECTEC’s Participation in WAT-2021
Zar Zar Hlaing, Ye Kyaw Thu, Thazin Myint Oo, Mya Ei San, Sasiporn Usanavasin, Ponrudee Netisopakul, Thepchai Supnithi
Abstract
In this paper, we report the experimental results of Machine Translation models conducted by a NECTEC team for the translation tasks of WAT-2021. Basically, our models are based on neural methods for both directions of English-Myanmar and Myanmar-English language pairs. Most of the existing Neural Machine Translation (NMT) models mainly focus on the conversion of sequential data and do not directly use syntactic information. However, we conduct multi-source neural machine translation (NMT) models using the multilingual corpora such as string data corpus, tree data corpus, or POS-tagged data corpus. The multi-source translation is an approach to exploit multiple inputs (e.g. in two different formats) to increase translation accuracy. The RNN-based encoder-decoder model with attention mechanism and transformer architectures have been carried out for our experiment. The experimental results showed that the proposed models of RNN-based architecture outperform the baseline model for English-to-Myanmar translation task, and the multi-source and shared-multi-source transformer models yield better translation results than the baseline.- Anthology ID:
- 2021.wat-1.6
- Volume:
- Proceedings of the 8th Workshop on Asian Translation (WAT2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Toshiaki Nakazawa, Hideki Nakayama, Isao Goto, Hideya Mino, Chenchen Ding, Raj Dabre, Anoop Kunchukuttan, Shohei Higashiyama, Hiroshi Manabe, Win Pa Pa, Shantipriya Parida, Ondřej Bojar, Chenhui Chu, Akiko Eriguchi, Kaori Abe, Yusuke Oda, Katsuhito Sudoh, Sadao Kurohashi, Pushpak Bhattacharyya
- Venue:
- WAT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 74–82
- Language:
- URL:
- https://aclanthology.org/2021.wat-1.6
- DOI:
- 10.18653/v1/2021.wat-1.6
- Bibkey:
- Cite (ACL):
- Zar Zar Hlaing, Ye Kyaw Thu, Thazin Myint Oo, Mya Ei San, Sasiporn Usanavasin, Ponrudee Netisopakul, and Thepchai Supnithi. 2021. NECTEC’s Participation in WAT-2021. In Proceedings of the 8th Workshop on Asian Translation (WAT2021), pages 74–82, Online. Association for Computational Linguistics.
- Cite (Informal):
- NECTEC’s Participation in WAT-2021 (Hlaing et al., WAT 2021)
- Copy Citation:
- PDF:
- https://aclanthology.org/2021.wat-1.6.pdf
Export citation
@inproceedings{hlaing-etal-2021-nectecs,
title = "{NECTEC}{'}s Participation in {WAT}-2021",
author = "Hlaing, Zar Zar and
Thu, Ye Kyaw and
Myint Oo, Thazin and
Ei San, Mya and
Usanavasin, Sasiporn and
Netisopakul, Ponrudee and
Supnithi, Thepchai",
editor = "Nakazawa, Toshiaki and
Nakayama, Hideki and
Goto, Isao and
Mino, Hideya and
Ding, Chenchen and
Dabre, Raj and
Kunchukuttan, Anoop and
Higashiyama, Shohei and
Manabe, Hiroshi and
Pa, Win Pa and
Parida, Shantipriya and
Bojar, Ond{\v{r}}ej and
Chu, Chenhui and
Eriguchi, Akiko and
Abe, Kaori and
Oda, Yusuke and
Sudoh, Katsuhito and
Kurohashi, Sadao and
Bhattacharyya, Pushpak",
booktitle = "Proceedings of the 8th Workshop on Asian Translation (WAT2021)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.wat-1.6",
doi = "10.18653/v1/2021.wat-1.6",
pages = "74--82",
abstract = "In this paper, we report the experimental results of Machine Translation models conducted by a NECTEC team for the translation tasks of WAT-2021. Basically, our models are based on neural methods for both directions of English-Myanmar and Myanmar-English language pairs. Most of the existing Neural Machine Translation (NMT) models mainly focus on the conversion of sequential data and do not directly use syntactic information. However, we conduct multi-source neural machine translation (NMT) models using the multilingual corpora such as string data corpus, tree data corpus, or POS-tagged data corpus. The multi-source translation is an approach to exploit multiple inputs (e.g. in two different formats) to increase translation accuracy. The RNN-based encoder-decoder model with attention mechanism and transformer architectures have been carried out for our experiment. The experimental results showed that the proposed models of RNN-based architecture outperform the baseline model for English-to-Myanmar translation task, and the multi-source and shared-multi-source transformer models yield better translation results than the baseline.",
}
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%0 Conference Proceedings %T NECTEC’s Participation in WAT-2021 %A Hlaing, Zar Zar %A Thu, Ye Kyaw %A Myint Oo, Thazin %A Ei San, Mya %A Usanavasin, Sasiporn %A Netisopakul, Ponrudee %A Supnithi, Thepchai %Y Nakazawa, Toshiaki %Y Nakayama, Hideki %Y Goto, Isao %Y Mino, Hideya %Y Ding, Chenchen %Y Dabre, Raj %Y Kunchukuttan, Anoop %Y Higashiyama, Shohei %Y Manabe, Hiroshi %Y Pa, Win Pa %Y Parida, Shantipriya %Y Bojar, Ondřej %Y Chu, Chenhui %Y Eriguchi, Akiko %Y Abe, Kaori %Y Oda, Yusuke %Y Sudoh, Katsuhito %Y Kurohashi, Sadao %Y Bhattacharyya, Pushpak %S Proceedings of the 8th Workshop on Asian Translation (WAT2021) %D 2021 %8 August %I Association for Computational Linguistics %C Online %F hlaing-etal-2021-nectecs %X In this paper, we report the experimental results of Machine Translation models conducted by a NECTEC team for the translation tasks of WAT-2021. Basically, our models are based on neural methods for both directions of English-Myanmar and Myanmar-English language pairs. Most of the existing Neural Machine Translation (NMT) models mainly focus on the conversion of sequential data and do not directly use syntactic information. However, we conduct multi-source neural machine translation (NMT) models using the multilingual corpora such as string data corpus, tree data corpus, or POS-tagged data corpus. The multi-source translation is an approach to exploit multiple inputs (e.g. in two different formats) to increase translation accuracy. The RNN-based encoder-decoder model with attention mechanism and transformer architectures have been carried out for our experiment. The experimental results showed that the proposed models of RNN-based architecture outperform the baseline model for English-to-Myanmar translation task, and the multi-source and shared-multi-source transformer models yield better translation results than the baseline. %R 10.18653/v1/2021.wat-1.6 %U https://aclanthology.org/2021.wat-1.6 %U https://doi.org/10.18653/v1/2021.wat-1.6 %P 74-82
Markdown (Informal)
[NECTEC’s Participation in WAT-2021](https://aclanthology.org/2021.wat-1.6) (Hlaing et al., WAT 2021)
- NECTEC’s Participation in WAT-2021 (Hlaing et al., WAT 2021)
ACL
- Zar Zar Hlaing, Ye Kyaw Thu, Thazin Myint Oo, Mya Ei San, Sasiporn Usanavasin, Ponrudee Netisopakul, and Thepchai Supnithi. 2021. NECTEC’s Participation in WAT-2021. In Proceedings of the 8th Workshop on Asian Translation (WAT2021), pages 74–82, Online. Association for Computational Linguistics.