Continuous Rating as Reliable Human Evaluation of Simultaneous Speech Translation
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Abstract (arXiv)
Simultaneous speech translation (SST) can be evaluated on simulated online events where human evaluators watch subtitled videos and continuously express their satisfaction by pressing buttons (so called Continuous Rating). Continuous Rating is easy to collect, but little is known about its reliability, or relation to comprehension of foreign language document by SST users. In this paper, we contrast Continuous Rating with factual questionnaires on judges with different levels of source language knowledge. Our results show that Continuous Rating is easy and reliable SST quality assessment if the judges have at least limited knowledge of the source language. Our study indicates users' preferences on subtitle layout and presentation style and, most importantly, provides a significant evidence that users with advanced source language knowledge prefer low latency over fewer re-translations.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 12 of its 30 references that are in the map. First posted on arXiv 4 Mar 2022.
Builds on
12 of 30 references.
References · 12
- 2017Attention Is All You Needcited by 59,932
- 2018Marian: Fast Neural Machine Translation in C++cited by 202
- 2019Monotonic Infinite Lookback Attention for Simultaneous Machine Translationcited by 103
- 2016Learning to Translate in Real-time with Neural Machine Translationcited by 97
- 2019Simpler and Faster Learning of Adaptive Policies for Simultaneous Translationcited by 36
- 2018Low-Latency Neural Speech Translationcited by 35
- 2020Re-translation versus Streaming for Simultaneous Translationcited by 30
- 2019Thinking Slow about Latency Evaluation for Simultaneous Machine Translationcited by 28
- 2018You May Not Need Attentioncited by 7
- 2019DuTongChuan: Context-aware Translation Model for Simultaneous Interpretingcited by 6
- 2020Online Versus Offline NMT Quality: An In-depth Analysis on English-German and German-Englishcited by 6
- 2020Presenting Simultaneous Translation in Limited Spacecited by 4
Built on by
Citing papers per year: 2022: 1; 2023: 0; 2024: 3; 2025: 1; 2026: 1.
6 papers.
- 2024How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?cited by 17
- 2024Towards Achieving Human Parity on End-to-end Simultaneous Speech Translation via LLM Agentcited by 14
- 2022MT Metrics Correlate with Human Ratings of Simultaneous Speech Translationcited by 9
- 2024Evaluating the IWSLT2023 Speech Translation Tasks: Human Annotations, Automatic Metrics, and Segmentationcited by 5
- 2025Overview of the Sensemaking Task at the ELOQUENT 2025 Lab: LLMs as Teachers, Students and Evaluatorscited by 0
- 2026EviSI: An Evidence-Based Evaluation Agent for Simultaneous Interpretingcited by 0
- 2026EviSI: An Evidence-Based Evaluation Agent for Simultaneous Interpreting8 Sep 2026
- 2025Overview of the Sensemaking Task at the ELOQUENT 2025 Lab: LLMs as Teachers, Students and Evaluators16 Jul 2025
- 2024How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?24 Dec 2024
- 2024Towards Achieving Human Parity on End-to-end Simultaneous Speech Translation via LLM Agent31 Jul 2024
- 2024Evaluating the IWSLT2023 Speech Translation Tasks: Human Annotations, Automatic Metrics, and Segmentation6 Jun 2024
- 2022MT Metrics Correlate with Human Ratings of Simultaneous Speech Translation16 Nov 2022