Abstract

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.

Highlights

  • Introduction over the target positions andCi a cost function for each target position i

  • Current translation latency evaluations (Ansari et al, 2020) are still performed at the sentence-level based on the conventional measures, Average Proportion (AP) (Cho and Esipova, 2016), Average Lagging (AL) (Ma et al, 2019) and Differentiable Average Lagging (DAL) (Cherry and Foster, 2019)

  • If we use a segmentation-free model whose output is a single text stream, stream-level latency meaproblem, we propose to multiply the cost of a write operation

Read more

Summary

A Reproducibility of proposed measures

The code for the proposed latency measures, as well as all the translations have been published 1. A script is included to reproduce the results reported in the paper. Efficient Wait-k Models for Simultaneous Machine Translation. ON-TRAC consortium for end-to-end and simultaneous speech translation challenge tasks at IWSLT 2020.

B MT System
C Segmenter System
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call