| Challenge: | Existing models for automatic subtitling generate subtitles in the target language along with their timestamps. |
| Approach: | They propose a direct speech translation model that generates subtitles in the target language along with their timestamps with a single model. |
| Outcome: | The proposed model outperforms a cascade system on 7 language pairs and on new benchmarks. |
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SBAAM! Eliminating Transcript Dependency in Automatic Subtitling (2024.acl-long)
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| Challenge: | Subtitling is a crucial task for enhancing the accessibility of audiovisual content and relying on automatic transcripts for the three subtasks is uncharted territory. |
| Approach: | They propose a model capable of producing automatic subtitles, completely eliminating any dependence on intermediate transcripts also for timestamp prediction. |
| Outcome: | Experimental results show that the proposed model eliminates the need for intermediate transcripts for timestamp prediction across multiple language pairs and diverse conditions. |
Dodging the Data Bottleneck: Automatic Subtitling with Automatically Segmented ST Corpora (2022.aacl-short)
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| Challenge: | Existing models for subtitling require parallel data paired with audio inputs and textual translations. |
| Approach: | They propose to convert existing ST corpora into SubST resources without human intervention by exploiting audio and text in a multimodal fashion. |
| Outcome: | The proposed model achieves high segmentation quality in zero-shot conditions with manual and automatic segmentation. |
ELITR Multilingual Live Subtitling: Demo and Strategy (2021.eacl-demos)
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Ondřej Bojar, Dominik Macháček, Sangeet Sagar, Otakar Smrž, Jonáš Kratochvíl, Peter Polák, Ebrahim Ansari, Mohammad Mahmoudi, Rishu Kumar, Dario Franceschini, Chiara Canton, Ivan Simonini, Thai-Son Nguyen, Felix Schneider, Sebastian Stüker, Alex Waibel, Barry Haddow, Rico Sennrich, Philip Williams
| Challenge: | Using a prototype, we present an automatic speech translation system for live subtitling of conference speech . the system is routinely tested in recognizing English, Czech, and German speech - and presenting it simultaneously into 42 target languages. |
| Approach: | They propose an automatic speech translation system aimed at live subtitling of conference presentations. |
| Outcome: | The proposed system is a working prototype that is routinely tested in recognizing English, Czech, and German speech and presenting it translated simultaneously into 42 target languages. |
MuST-Cinema: a Speech-to-Subtitles corpus (2020.lrec-1)
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| Challenge: | Existing methods for subtitling are laborious and costly, says aaron sanchez . he says the current methods are laboriously complex and require manual work . |
| Approach: | They propose to use TED subtitles to build a multilingual speech translation corpus . they propose to annotate existing subtitling corpora with subtitle breaks . |
| Outcome: | The proposed model can be used to segment sentences into subtitles and reduces human work . the proposed model reduces the time and cost of human subtitling tasks . |
Tutorial: End-to-End Speech Translation (2021.eacl-tutorials)
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| Challenge: | Speech translation is the translation of speech in one language typically to text in another, traditionally accomplished through a combination of automatic speech recognition and machine translation. |
| Approach: | This tutorial introduces the techniques used in cutting-edge research on speech translation. |
| Outcome: | The proposed models achieve state-of-the-art performance with end-to-end speech translation for both high- and low-resource languages. |
End-to-End Multilingual Automatic Dubbing via Duration-based Translation with Large Language Models (2025.emnlp-demos)
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| Challenge: | Automatic dubbing (AD) aims to replace the original speech with translated speech that maintains precise temporal alignment (isochrony). |
| Approach: | They propose an end-to-end automatic dubbing framework that leverages large language models to integrate translation and timing control seamlessly. |
| Outcome: | The proposed framework achieves up to 24% relative gains on English, Spanish, and Korean language pairs while maintaining competitive translation quality measured by COMET scores. |
Unsupervised Subtitle Segmentation with Masked Language Models (2023.acl-short)
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| Challenge: | Existing methods to improve subtitle segmentation are based on character counting and linguistically correct segmentation. |
| Approach: | They propose a method where subtitle breaks are predicted according to likelihood of punctuation . their approach is highly portable across languages and domains . |
| Outcome: | The proposed method obtained competitive results in terms of segmentation accuracy across metrics while also fully preserving the original text and complying with length constraints. |
Simultaneous Translation (2020.emnlp-tutorials)
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| Challenge: | Simultaneous translation is a problem that has long been considered one of the hardest problems in AI . this tutorial will provide a deep understanding of the history and the recent advances in simultaneous translation. |
| Approach: | This tutorial will examine the design and evaluation of policies for simultaneous translation . it will provide an overview of the history and recent advances in simultaneous translation. |
| Outcome: | This tutorial will examine the design and evaluation of policies for simultaneous translation . |
Cross-Lingual Sentence Compression for Length-Constrained Subtitles in Low-Resource Settings (2025.coling-main)
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| Challenge: | a new system for sentence compression is developed for broadcast and live media . the goal is to reduce the on-screen visual constraints of the text . |
| Approach: | They develop a machine translation and sentence compression system that trains on openly available parallel corpora organized by compression ratios. |
| Outcome: | The proposed system preserves high semantic meaning and metric evaluations for compressed contexts. |
Direct Segmentation Models for Streaming Speech Translation (2020.emnlp-main)
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Javier Iranzo-Sánchez, Adrià Giménez Pastor, Joan Albert Silvestre-Cerdà, Pau Baquero-Arnal, Jorge Civera Saiz, Alfons Juan
| Challenge: | Existing approaches to stream ST combine advances in ASR and MT to achieve high quality translations without compromising the speed of the system. |
| Approach: | They propose to concatenate an Automatic Speech Recognition system followed by a Machine Translation system. |
| Outcome: | The proposed models improve on the Europarl-ST dataset on the BLEU score. |