mlsanityData to 7 Oct 2026

Stream-level Latency Evaluation for Simultaneous Machine Translation

arXiv 2104.08817 ↗First posted 18 Apr 2021cs.CLcited by 12

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Abstract (arXiv)

Simultaneous machine translation has recently gained traction thanks to significant quality improvements and the advent of streaming applications. Simultaneous translation systems need to find a trade-off between translation quality and response time, and with this purpose multiple latency measures have been proposed. However, latency evaluations for simultaneous translation are estimated at the sentence level, not taking into account the sequential nature of a streaming scenario. Indeed, these sentence-level latency measures are not well suited for continuous stream translation resulting in figures that are not coherent with the simultaneous translation policy of the system being assessed. This work proposes a stream-level adaptation of the current latency measures based on a re-segmentation approach applied to the output translation, that is successfully evaluated on streaming conditions for a reference IWSLT task.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 5 of its 17 references that are in the map. First posted on arXiv 18 Apr 2021.

Builds on

5 of 17 references.

References · 5

  1. 2017Attention Is All You Needcited by 59,932
  2. 2016Can neural machine translation do simultaneous translation?cited by 85
  3. 2020Efficient Wait-k Models for Simultaneous Machine Translationcited by 59
  4. 2019Thinking Slow about Latency Evaluation for Simultaneous Machine Translationcited by 28
  5. 2020ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020cited by 9

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Cited by year

Citing papers per year: 2022: 2; 2023: 5; 2024: 4; 2025: 1.

12 papers.