# Re-Translation Strategies For Long Form, Simultaneous, Spoken Language Translation

arXiv: https://arxiv.org/abs/1912.03393 · HTML: https://mlsanity.com/paper/1912.03393/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2019.

## Built on by (most cited first, 25 of 25)

- [Direct Simultaneous Speech-to-Text Translation Assisted by Synchronized Streaming ASR](https://mlsanity.com/paper/2106.06636/) (2021)
- [Learning When to Translate for Streaming Speech](https://mlsanity.com/paper/2109.07368/) (2021)
- [How "Real" is Your Real-Time Simultaneous Speech-to-Text Translation System?](https://mlsanity.com/paper/2412.18495/) (2024)
- [Blockwise Streaming Transformer for Spoken Language Understanding and Simultaneous Speech Translation](https://mlsanity.com/paper/2204.08920/) (2022)
- [Streaming Models for Joint Speech Recognition and Translation](https://mlsanity.com/paper/2101.09149/) (2021)
- [Lost in Interpreting: Speech Translation from Source or Interpreter?](https://mlsanity.com/paper/2106.09343/) (2021)
- [Simultaneous Machine Translation with Large Language Models](https://mlsanity.com/paper/2309.06706/) (2023)
- [Don't Discard Fixed-Window Audio Segmentation in Speech-to-Text Translation](https://mlsanity.com/paper/2210.13363/) (2022)
- [Simultaneous Translation for Unsegmented Input: A Sliding Window Approach](https://mlsanity.com/paper/2210.09754/) (2022)
- [Align, Write, Re-order: Explainable End-to-End Speech Translation via Operation Sequence Generation](https://mlsanity.com/paper/2211.05967/) (2022)
- [Simulstream: Open-Source Toolkit for Evaluation and Demonstration of Streaming Speech-to-Text Translation Systems](https://mlsanity.com/paper/2512.17648/) (2025)
- [Optimizing Latency for Online Video CaptioningUsing Audio-Visual Transformers](https://mlsanity.com/paper/2108.02147/) (2021)
- [Segmentation-Free Streaming Machine Translation](https://mlsanity.com/paper/2309.14823/) (2023)
- [Visualization: the missing factor in Simultaneous Speech Translation](https://mlsanity.com/paper/2111.00514/) (2021)
- [Improving Stability in Simultaneous Speech Translation: A Revision-Controllable Decoding Approach](https://mlsanity.com/paper/2310.04399/) (2023)
- [Language Model is a Branch Predictor for Simultaneous Machine Translation](https://mlsanity.com/paper/2312.14488/) (2023)
- [MeetDot: Videoconferencing with Live Translation Captions](https://mlsanity.com/paper/2109.09577/) (2021)
- [When Only Time Will Tell: Interpreting How Transformers Process Local Ambiguities Through the Lens of Restart-Incrementality](https://mlsanity.com/paper/2402.13113/) (2024)
- [NAIST Simultaneous Speech Translation System for IWSLT 2024](https://mlsanity.com/paper/2407.00826/) (2024)
- [Rethinking the Reasonability of the Test Set for Simultaneous Machine Translation](https://mlsanity.com/paper/2303.00969/) (2023)
- [Overcoming Latency Bottlenecks in On-Device Speech Translation: A Cascaded Approach with Alignment-Based Streaming MT](https://mlsanity.com/paper/2508.13358/) (2025)
- [Self-Speculative Biased Decoding for Faster Re-Translation](https://mlsanity.com/paper/2509.21740/) (2025)
- [MLLP-VRAIN UPV system for the IWSLT 2026 Simultaneous Speech Translation task](https://mlsanity.com/paper/2606.17255/) (2026)
- [Do LLMs Need Architectural Changes for Simultaneous Speech Translation? A Prefix-to-Prefix Data Driven Approach](https://mlsanity.com/paper/2607.13158/) (2026)
- [ReSCUE: Re-translation with Sentence Commitment for Unsegmented Long-Form Simultaneous Sign Language Translation](https://mlsanity.com/paper/2610.03022/) (2026)

## Abstract (arXiv)

We investigate the problem of simultaneous machine translation of long-form speech content. We target a continuous speech-to-text scenario, generating translated captions for a live audio feed, such as a lecture or play-by-play commentary. As this scenario allows for revisions to our incremental translations, we adopt a re-translation approach to simultaneous translation, where the source is repeatedly translated from scratch as it grows. This approach naturally exhibits very low latency and high final quality, but at the cost of incremental instability as the output is continuously refined. We experiment with a pipeline of industry-grade speech recognition and translation tools, augmented with simple inference heuristics to improve stability. We use TED Talks as a source of multilingual test data, developing our techniques on English-to-German spoken language translation. Our minimalist approach to simultaneous translation allows us to easily scale our final evaluation to six more target languages, dramatically improving incremental stability for all of them.