Information-Transport-based Policy for Simultaneous Translation (2022.emnlp-main)
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| Challenge: | Simultaneous translation (ST) outputs translation while receiving source inputs . low latency restriction restricts ST to translating target tokens based on current received source tokens. |
| Approach: | They propose a system that outputs translation while receiving source inputs . it uses a read/write policy to decide whether to translate a target token or wait for the next source token . |
| Outcome: | The proposed model outperforms baselines and achieves state-of-the-art on text-to-text and speech-to text tasks. |
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| Challenge: | Existing methods to balance source and target information at the token level are limited by the number of received source tokens. |
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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. |
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| Challenge: | Existing methods to perform simultaneous speech-to-text translation ignore contextual information and suffer from low translation quality. |
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| Challenge: | Existing methods to perform adaptive and fixed translations lack evaluation before taking actions. |
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| Challenge: | Existing approaches to simultaneous speech-to-text translation suffer from error propagation and extra latency. |
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Hierarchical Policy Optimization for Simultaneous Translation of Unbounded Speech (2026.acl-long)
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| Challenge: | Existing synthesis methods cannot guarantee data quality. |
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SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation (2020.aacl-main)
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| Challenge: | Using end-to-end Simultaneous text translation, we adapt wait-k and monotonic multihead attention to end- to-end simultaneous speech translation. |
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| Challenge: | Recent work on simultaneous translation is difficult because of its latency and quality. |
| Approach: | They propose a supervised-learning framework to learn adaptive policies from parallel text sequences . they use a model that predicts when a target word is read or WRITE if context provides enough information . |
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InfiniSST: Simultaneous Translation of Unbounded Speech with Large Language Model (2025.findings-acl)
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| Challenge: | Existing models for simultaneous speech translation assume pre-segmented speech, limiting their real-world applicability. |
| Approach: | They propose a multi-turn dialogue task that can translate unbounded streaming speech . they construct translation trajectories and robust segments from MuST-C with multi-latency augmentation during training and develop a cache management strategy to facilitate efficient inference. |
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