Findings of the WMT 2022 Shared Task on Efficient Translation
Kenneth Heafield, Biao Zhang, Graeme Nail, Jelmer Van Der Linde, Nikolay Bogoychev
Abstract
The machine translation efficiency task challenges participants to make their systems faster and smaller with minimal impact on translation quality. How much quality to sacrifice for efficiency depends upon the application, so participants were encouraged to make multiple submissions covering the space of trade-offs. In total, there were 76 submissions from 5 teams. The task covers GPU, single-core CPU, and multi-core CPU hardware tracks as well as batched throughput or single-sentence latency conditions. Submissions showed hundreds of millions of words can be translated for a dollar, average latency is 3.5–25 ms, and models fit in 7.5–900 MB.- Anthology ID:
- 2022.wmt-1.4
- Volume:
- Proceedings of the Seventh Conference on Machine Translation (WMT)
- Month:
- December
- Year:
- 2022
- Address:
- Abu Dhabi, United Arab Emirates (Hybrid)
- Venue:
- WMT
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 100–108
- Language:
- URL:
- https://aclanthology.org/2022.wmt-1.4
- DOI:
- Cite (ACL):
- Kenneth Heafield, Biao Zhang, Graeme Nail, Jelmer Van Der Linde, and Nikolay Bogoychev. 2022. Findings of the WMT 2022 Shared Task on Efficient Translation. In Proceedings of the Seventh Conference on Machine Translation (WMT), pages 100–108, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.
- Cite (Informal):
- Findings of the WMT 2022 Shared Task on Efficient Translation (Heafield et al., WMT 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.wmt-1.4.pdf