# MuST-Cinema: a Speech-to-Subtitles corpus

arXiv: https://arxiv.org/abs/2002.10829 · HTML: https://mlsanity.com/paper/2002.10829/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2020.

## Built on by (most cited first, 17 of 17)

- [Isochrony-Aware Neural Machine Translation for Automatic Dubbing](https://mlsanity.com/paper/2112.08548/) (2021)
- [SubER: A Metric for Automatic Evaluation of Subtitle Quality](https://mlsanity.com/paper/2205.05805/) (2022)
- [Simultaneous Speech Translation for Live Subtitling: from Delay to Display](https://mlsanity.com/paper/2107.08807/) (2021)
- [Between Flexibility and Consistency: Joint Generation of Captions and Subtitles](https://mlsanity.com/paper/2107.06246/) (2021)
- [Direct Speech Translation for Automatic Subtitling](https://mlsanity.com/paper/2209.13192/) (2022)
- [Direct Models for Simultaneous Translation and Automatic Subtitling: FBK@IWSLT2023](https://mlsanity.com/paper/2309.15554/) (2023)
- [ANIM-400K: A Large-Scale Dataset for Automated End-To-End Dubbing of Video](https://mlsanity.com/paper/2401.05314/) (2024)
- [Joint Generation of Captions and Subtitles with Dual Decoding](https://mlsanity.com/paper/2205.06522/) (2022)
- [Dodging the Data Bottleneck: Automatic Subtitling with Automatically Segmented ST Corpora](https://mlsanity.com/paper/2209.10608/) (2022)
- [Evaluating Subtitle Segmentation for End-to-end Generation Systems](https://mlsanity.com/paper/2205.09360/) (2022)
- [MELD-ST: An Emotion-aware Speech Translation Dataset](https://mlsanity.com/paper/2405.13233/) (2024)
- [Multilingual Multimodality: A Taxonomical Survey of Datasets, Techniques, Challenges and Opportunities](https://mlsanity.com/paper/2210.16960/) (2022)
- [SBAAM! Eliminating Transcript Dependency in Automatic Subtitling](https://mlsanity.com/paper/2405.10741/) (2024)
- [MockConf: A Student Interpretation Dataset: Analysis, Word- and Span-level Alignment and Baselines](https://mlsanity.com/paper/2506.04848/) (2025)
- [HOMURA: Taming the Sand-Glass for Time-Constrained LLM Translation via Reinforcement Learning](https://mlsanity.com/paper/2601.10187/) (2026)
- [V-SAT: Video Subtitle Annotation Tool](https://mlsanity.com/paper/2510.24180/) (2025)
- [Breaking Babel: A Self-Evolving Multi-Agent System for Long-Form Subtitle Translation](https://mlsanity.com/paper/2609.38660/) (2026)

## Abstract (arXiv)

Growing needs in localising audiovisual content in multiple languages through subtitles call for the development of automatic solutions for human subtitling. Neural Machine Translation (NMT) can contribute to the automatisation of subtitling, facilitating the work of human subtitlers and reducing turn-around times and related costs. NMT requires high-quality, large, task-specific training data. The existing subtitling corpora, however, are missing both alignments to the source language audio and important information about subtitle breaks. This poses a significant limitation for developing efficient automatic approaches for subtitling, since the length and form of a subtitle directly depends on the duration of the utterance. In this work, we present MuST-Cinema, a multilingual speech translation corpus built from TED subtitles. The corpus is comprised of (audio, transcription, translation) triplets. Subtitle breaks are preserved by inserting special symbols. We show that the corpus can be used to build models that efficiently segment sentences into subtitles and propose a method for annotating existing subtitling corpora with subtitle breaks, conforming to the constraint of length.