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:
- 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)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2020.wmt-1.29.pdf