NLPRL System for Very Low Resource Supervised Machine Translation
Rupjyoti Baruah, Rajesh Kumar Mundotiya, Amit Kumar, Anil kumar Singh
Abstract
This paper describes the results of the system that we used for the WMT20 very low resource (VLR) supervised MT shared task. For our experiments, we use a byte-level version of BPE, which requires a base vocabulary of size 256 only. BPE based models are a kind of sub-word models. Such models try to address the Out of Vocabulary (OOV) word problem by performing word segmentation so that segments correspond to morphological units. They are also reported to work across different languages, especially similar languages due to their sub-word nature. Based on BLEU cased score, our NLPRL systems ranked ninth for HSB to GER and tenth in GER to HSB translation scenario.- Anthology ID:
- 2020.wmt-1.126
- Volume:
- Proceedings of the Fifth Conference on Machine Translation
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Editors:
- Loïc Barrault, Ondřej Bojar, Fethi Bougares, Rajen Chatterjee, Marta R. Costa-jussà, Christian Federmann, Mark Fishel, Alexander Fraser, Yvette Graham, Paco Guzman, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Makoto Morishita, Christof Monz, Masaaki Nagata, Toshiaki Nakazawa, Matteo Negri
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1075–1078
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.126
- DOI:
- Bibkey:
- Cite (ACL):
- Rupjyoti Baruah, Rajesh Kumar Mundotiya, Amit Kumar, and Anil kumar Singh. 2020. NLPRL System for Very Low Resource Supervised Machine Translation. In Proceedings of the Fifth Conference on Machine Translation, pages 1075–1078, Online. Association for Computational Linguistics.
- Cite (Informal):
- NLPRL System for Very Low Resource Supervised Machine Translation (Baruah et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.126.pdf
- Video:
- https://slideslive.com/38939625
Export citation
@inproceedings{baruah-etal-2020-nlprl,
title = "{NLPRL} System for Very Low Resource Supervised Machine Translation",
author = "Baruah, Rupjyoti and
Mundotiya, Rajesh Kumar and
Kumar, Amit and
Singh, Anil kumar",
editor = {Barrault, Lo{\"\i}c and
Bojar, Ond{\v{r}}ej and
Bougares, Fethi and
Chatterjee, Rajen and
Costa-juss{\`a}, Marta R. and
Federmann, Christian and
Fishel, Mark and
Fraser, Alexander and
Graham, Yvette and
Guzman, Paco and
Haddow, Barry and
Huck, Matthias and
Yepes, Antonio Jimeno and
Koehn, Philipp and
Martins, Andr{\'e} and
Morishita, Makoto and
Monz, Christof and
Nagata, Masaaki and
Nakazawa, Toshiaki and
Negri, Matteo},
booktitle = "Proceedings of the Fifth Conference on Machine Translation",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.wmt-1.126",
pages = "1075--1078",
abstract = "This paper describes the results of the system that we used for the WMT20 very low resource (VLR) supervised MT shared task. For our experiments, we use a byte-level version of BPE, which requires a base vocabulary of size 256 only. BPE based models are a kind of sub-word models. Such models try to address the Out of Vocabulary (OOV) word problem by performing word segmentation so that segments correspond to morphological units. They are also reported to work across different languages, especially similar languages due to their sub-word nature. Based on BLEU cased score, our NLPRL systems ranked ninth for HSB to GER and tenth in GER to HSB translation scenario.",
}
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<abstract>This paper describes the results of the system that we used for the WMT20 very low resource (VLR) supervised MT shared task. For our experiments, we use a byte-level version of BPE, which requires a base vocabulary of size 256 only. BPE based models are a kind of sub-word models. Such models try to address the Out of Vocabulary (OOV) word problem by performing word segmentation so that segments correspond to morphological units. They are also reported to work across different languages, especially similar languages due to their sub-word nature. Based on BLEU cased score, our NLPRL systems ranked ninth for HSB to GER and tenth in GER to HSB translation scenario.</abstract>
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%0 Conference Proceedings %T NLPRL System for Very Low Resource Supervised Machine Translation %A Baruah, Rupjyoti %A Mundotiya, Rajesh Kumar %A Kumar, Amit %A Singh, Anil kumar %Y Barrault, Loïc %Y Bojar, Ondřej %Y Bougares, Fethi %Y Chatterjee, Rajen %Y Costa-jussà, Marta R. %Y Federmann, Christian %Y Fishel, Mark %Y Fraser, Alexander %Y Graham, Yvette %Y Guzman, Paco %Y Haddow, Barry %Y Huck, Matthias %Y Yepes, Antonio Jimeno %Y Koehn, Philipp %Y Martins, André %Y Morishita, Makoto %Y Monz, Christof %Y Nagata, Masaaki %Y Nakazawa, Toshiaki %Y Negri, Matteo %S Proceedings of the Fifth Conference on Machine Translation %D 2020 %8 November %I Association for Computational Linguistics %C Online %F baruah-etal-2020-nlprl %X This paper describes the results of the system that we used for the WMT20 very low resource (VLR) supervised MT shared task. For our experiments, we use a byte-level version of BPE, which requires a base vocabulary of size 256 only. BPE based models are a kind of sub-word models. Such models try to address the Out of Vocabulary (OOV) word problem by performing word segmentation so that segments correspond to morphological units. They are also reported to work across different languages, especially similar languages due to their sub-word nature. Based on BLEU cased score, our NLPRL systems ranked ninth for HSB to GER and tenth in GER to HSB translation scenario. %U https://aclanthology.org/2020.wmt-1.126 %P 1075-1078
Markdown (Informal)
[NLPRL System for Very Low Resource Supervised Machine Translation](https://aclanthology.org/2020.wmt-1.126) (Baruah et al., WMT 2020)
- NLPRL System for Very Low Resource Supervised Machine Translation (Baruah et al., WMT 2020)
ACL
- Rupjyoti Baruah, Rajesh Kumar Mundotiya, Amit Kumar, and Anil kumar Singh. 2020. NLPRL System for Very Low Resource Supervised Machine Translation. In Proceedings of the Fifth Conference on Machine Translation, pages 1075–1078, Online. Association for Computational Linguistics.