Lost in Interpreting: Speech Translation from Source or Interpreter?
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Abstract (arXiv)
Interpreters facilitate multi-lingual meetings but the affordable set of languages is often smaller than what is needed. Automatic simultaneous speech translation can extend the set of provided languages. We investigate if such an automatic system should rather follow the original speaker, or an interpreter to achieve better translation quality at the cost of increased delay. To answer the question, we release Europarl Simultaneous Interpreting Corpus (ESIC), 10 hours of recordings and transcripts of European Parliament speeches in English, with simultaneous interpreting into Czech and German. We evaluate quality and latency of speaker-based and interpreter-based spoken translation systems from English to Czech. We study the differences in implicit simplification and summarization of the human interpreter compared to a machine translation system trained to shorten the output to some extent. Finally, we perform human evaluation to measure information loss of each of these approaches.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 17 Jun 2021.
Builds on
11 of 30 references.
References · 11
- 2017Attention Is All You Needcited by 59,932
- 2018Marian: Fast Neural Machine Translation in C++cited by 202
- 2019Europarl-ST: A Multilingual Corpus For Speech Translation Of Parliamentary Debatescited by 114
- 2018BLEU is Not Suitable for the Evaluation of Text Simplificationcited by 78
- 2016Controlling Output Length in Neural Encoder-Decoderscited by 75
- 2019Positional Encoding to Control Output Sequence Lengthcited by 56
- 2018Low-Latency Neural Speech Translationcited by 35
- 2019Re-Translation Strategies For Long Form, Simultaneous, Spoken Language Translationcited by 24
- 2020Super-Human Performance in Online Low-latency Recognition of Conversational Speechcited by 20
- 2019Multilingual Neural Machine Translation for Zero-Resource Languagescited by 5
- 2020ELITR Non-Native Speech Translation at IWSLT 2020cited by 5
Built on by
Citing papers per year: 2022: 3; 2023: 4; 2024: 2; 2025: 1.
10 papers.
- 2023Turning Whisper into Real-Time Transcription Systemcited by 23
- 2022Non-Parametric Domain Adaptation for End-to-End Speech Translationcited by 12
- 2022MT Metrics Correlate with Human Ratings of Simultaneous Speech Translationcited by 9
- 2022Simultaneous Translation for Unsegmented Input: A Sliding Window Approachcited by 6
- 2024Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Paircited by 3
- 2023NAIST-SIC-Aligned: an Aligned English-Japanese Simultaneous Interpretation Corpuscited by 2
- 2023Robustness of Multi-Source MT to Transcription Errorscited by 2
- 2025MockConf: A Student Interpretation Dataset: Analysis, Word- and Span-level Alignment and Baselinescited by 1
- 2023Rethinking the Reasonability of the Test Set for Simultaneous Machine Translationcited by 0
- 2024Whispy: Adapting STT Whisper Models to Real-Time Environmentscited by 0
- 2025MockConf: A Student Interpretation Dataset: Analysis, Word- and Span-level Alignment and Baselines5 Jun 2025
- 2024Whispy: Adapting STT Whisper Models to Real-Time Environments6 May 2024
- 2024Simultaneous Interpretation Corpus Construction by Large Language Models in Distant Language Pair18 Apr 2024
- 2023Turning Whisper into Real-Time Transcription System27 Jul 2023
- 2023Robustness of Multi-Source MT to Transcription Errors26 May 2023
- 2023NAIST-SIC-Aligned: an Aligned English-Japanese Simultaneous Interpretation Corpus23 Apr 2023
- 2023Rethinking the Reasonability of the Test Set for Simultaneous Machine Translation2 Mar 2023
- 2022MT Metrics Correlate with Human Ratings of Simultaneous Speech Translation16 Nov 2022
- 2022Simultaneous Translation for Unsegmented Input: A Sliding Window Approach18 Oct 2022
- 2022Non-Parametric Domain Adaptation for End-to-End Speech Translation23 May 2022