Findings of the First Shared Task on Lifelong Learning Machine Translation
Loïc Barrault, Magdalena Biesialska, Marta R. Costa-jussà, Fethi Bougares, Olivier Galibert
Abstract
A lifelong learning system can adapt to new data without forgetting previously acquired knowledge. In this paper, we introduce the first benchmark for lifelong learning machine translation. For this purpose, we provide training, lifelong and test data sets for two language pairs: English-German and English-French. Additionally, we report the results of our baseline systems, which we make available to the public. The goal of this shared task is to encourage research on the emerging topic of lifelong learning machine translation.- Anthology ID:
- 2020.wmt-1.2
- 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:
- 56–64
- Language:
- URL:
- https://aclanthology.org/2020.wmt-1.2
- DOI:
- Cite (ACL):
- Loïc Barrault, Magdalena Biesialska, Marta R. Costa-jussà, Fethi Bougares, and Olivier Galibert. 2020. Findings of the First Shared Task on Lifelong Learning Machine Translation. In Proceedings of the Fifth Conference on Machine Translation, pages 56–64, Online. Association for Computational Linguistics.
- Cite (Informal):
- Findings of the First Shared Task on Lifelong Learning Machine Translation (Barrault et al., WMT 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.wmt-1.2.pdf