mlsanityData to 14 Oct 2026

Toward Machine Interpreting: Lessons from Human Interpreting Studies

arXiv 2508.07964 ↗First posted 11 Aug 2025cs.CLcited by 0

Not on the map. Why?

Abstract (arXiv)

Current speech translation systems, while having achieved impressive accuracies, are rather static in their behavior and do not adapt to real-world situations in ways human interpreters do. In order to improve their practical usefulness and enable interpreting-like experiences, a precise understanding of the nature of human interpreting is crucial. To this end, we discuss human interpreting literature from the perspective of the machine translation field, while considering both operational and qualitative aspects. We identify implications for the development of speech translation systems and argue that there is great potential to adopt many human interpreting principles using recent modeling techniques. We hope that our findings provide inspiration for closing the perceived usability gap, and can motivate progress toward true machine interpreting.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 37 of its 103 references that are in the map. First posted on arXiv 11 Aug 2025.

Builds on

37 of 103 references.

References · 37

  1. 2018Marian: Fast Neural Machine Translation in C++cited by 203
  2. 2020Speech Translation and the End-to-End Promise: Taking Stock of Where We Arecited by 77
  3. 2025Baichuan-Audio: A Unified Framework for End-to-End Speech Interactioncited by 68
  4. 2019Learning to Speak Fluently in a Foreign Language: Multilingual Speech Synthesis and Cross-Language Voice Cloningcited by 66
  5. 2024Language Model Can Listen While Speakingcited by 63
  6. 2023Benchmarking Machine Translation with Cultural Awarenesscited by 53
  7. 2021Keep it Simple: Unsupervised Simplification of Multi-Paragraph Textcited by 45
  8. 2019Controlling the Output Length of Neural Machine Translationcited by 36
  9. 2019Controlling Text Complexity in Neural Machine Translationcited by 32
  10. 2019Multimodal Machine Translation through Visuals and Speechcited by 29
  11. 2019Automatic Fact-guided Sentence Modificationcited by 28
  12. 2023Speech Translation with Large Language Models: An Industrial Practicecited by 28
25 more
  1. 2019Fluent Translations from Disfluent Speech in End-to-End Speech Translationcited by 22
  2. 2024Towards Cross-Cultural Machine Translation with Retrieval-Augmented Generation from Multilingual Knowledge Graphscited by 22
  3. 2020Simultaneous Machine Translation with Visual Contextcited by 17
  4. 2024How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?cited by 17
  5. 2023Large Language Models Meet Open-World Intent Discovery and Recognition: An Evaluation of ChatGPTcited by 16
  6. 2020Unmet Needs and Opportunities for Mobile Translation AIcited by 14
  7. 2024Towards Achieving Human Parity on End-to-end Simultaneous Speech Translation via LLM Agentcited by 14
  8. 2024Speech is More Than Words: Do Speech-to-Text Translation Systems Leverage Prosody?cited by 13
  9. 2018Neural Machine Translation of Text from Non-Native Speakerscited by 10
  10. 2020Fluent and Low-latency Simultaneous Speech-to-Speech Translation with Self-adaptive Trainingcited by 10
  11. 2024A Non-autoregressive Generation Framework for End-to-End Simultaneous Speech-to-Speech Translationcited by 10
  12. 2024SpeechQE: Estimating the Quality of Direct Speech Translationcited by 10
  13. 2021Laughing Heads: Can Transformers Detect What Makes a Sentence Funny?cited by 9
  14. 2024SeamlessExpressiveLM: Speech Language Model for Expressive Speech-to-Speech Translation with Chain-of-Thoughtcited by 9
  15. 2024Simpson's Paradox and the Accuracy-Fluency Tradeoff in Translationcited by 8
  16. 2025Translation in the Hands of Many:Centering Lay Users in Machine Translation Interactionscited by 8
  17. 2023Bridging Background Knowledge Gaps in Translation with Automatic Explicitationcited by 7
  18. 2024Speechworthy Instruction-tuned Language Modelscited by 7
  19. 2025SimulPL: Aligning Human Preferences in Simultaneous Machine Translationcited by 7
  20. 2024Simplifying Translations for Children: Iterative Simplification Considering Age of Acquisition with LLMscited by 5
  21. 2024Blending LLMs into Cascaded Speech Translation: KIT's Offline Speech Translation System for IWSLT 2024cited by 4
  22. 2022Domain Adaptation of Machine Translation with Crowdworkerscited by 3
  23. 2024CogErgLLM: Exploring Large Language Model Systems Design Perspective Using Cognitive Ergonomicscited by 3
  24. 2025Spatial Speech Translation: Translating Across Space With Binaural Hearablescited by 3
  25. 2016Machine Agency in Human-Machine Networks; Impacts and Trust Implicationscited by 2

Built on by

Not cited yet.