Grundkiewicz, Roman

Heafield, Kenneth

Zhu, Qianqian

English

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 53 submissions by 4 teams. There were 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 5--17 ms, and models fit in 7.5--150 MB

Edinburgh Research Explorer

Findings of the WMT 2021 Shared Task on Efficient Translation

https://www.research.ed.ac.uk/files/252207740/Findings_of_the_WMT_HEAFIELD_DOA05092021_VOR_CC_BY.pdf

Findings of the WMT 2021 Shared Task on Efficient Translation

Abstract

Similar works

Full text

Available Versions

Edinburgh Research Explorer