Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training
Christian Roest, Lukas Edman, Gosse Minnema, Kevin Kelly, Jennifer Spenader, Antonio Toral
Abstract
Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We present the results of our systems for the English–Inuktitut language pair for the WMT 2020 translation tasks. We investigated the importance of correct morphological segmentation, whether or not adding data from a related language (Greenlandic) helps, and whether using contextual word embeddings improves translation. While each method showed some promise, the results are mixed.- Anthology ID:
- 2020.wmt-1.29
- 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:
- 274–281
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.29
- DOI:
- Bibkey:
- Cite (ACL):
- Christian Roest, Lukas Edman, Gosse Minnema, Kevin Kelly, Jennifer Spenader, and Antonio Toral. 2020. Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training. In Proceedings of the Fifth Conference on Machine Translation, pages 274–281, Online. Association for Computational Linguistics.
- Cite (Informal):
- Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training (Roest et al., WMT 2020)
- Copy Citation:
- PDF:
- https://aclanthology.org/2020.wmt-1.29.pdf
- Video:
- https://slideslive.com/38939665
Export citation
@inproceedings{roest-etal-2020-machine,
title = "Machine Translation for {E}nglish{--}{I}nuktitut with Segmentation, Data Acquisition and Pre-Training",
author = "Roest, Christian and
Edman, Lukas and
Minnema, Gosse and
Kelly, Kevin and
Spenader, Jennifer and
Toral, Antonio",
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-1.29",
pages = "274--281",
abstract = "Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We present the results of our systems for the English{--}Inuktitut language pair for the WMT 2020 translation tasks. We investigated the importance of correct morphological segmentation, whether or not adding data from a related language (Greenlandic) helps, and whether using contextual word embeddings improves translation. While each method showed some promise, the results are mixed.",
}
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%0 Conference Proceedings %T Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training %A Roest, Christian %A Edman, Lukas %A Minnema, Gosse %A Kelly, Kevin %A Spenader, Jennifer %A Toral, Antonio %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 roest-etal-2020-machine %X Translating to and from low-resource polysynthetic languages present numerous challenges for NMT. We present the results of our systems for the English–Inuktitut language pair for the WMT 2020 translation tasks. We investigated the importance of correct morphological segmentation, whether or not adding data from a related language (Greenlandic) helps, and whether using contextual word embeddings improves translation. While each method showed some promise, the results are mixed. %U https://aclanthology.org/2020.wmt-1.29 %P 274-281
Markdown (Informal)
[Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training](https://aclanthology.org/2020.wmt-1.29) (Roest et al., WMT 2020)
- Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training (Roest et al., WMT 2020)
ACL
- Christian Roest, Lukas Edman, Gosse Minnema, Kevin Kelly, Jennifer Spenader, and Antonio Toral. 2020. Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training. In Proceedings of the Fifth Conference on Machine Translation, pages 274–281, Online. Association for Computational Linguistics.