Papers with Simultaneous machine translation
Stream-level Latency Evaluation for Simultaneous Machine Translation (2021.findings-emnlp)
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| Challenge: | Simultaneous machine translation systems need to find a trade-off between translation quality and response time. |
| Approach: | They propose to adapt existing translation latency measures to streaming scenarios by re-segmenting the output translation to take into account sequential nature of streaming scenarios. |
| Outcome: | The proposed measures are evaluated on a streaming task on simulated speech translation systems. |
Monotonic Infinite Lookback Attention for Simultaneous Machine Translation (P19-1)
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Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, Chung-Cheng Chiu, Semih Yavuz, Ruoming Pang, Wei Li, Colin Raffel
| Challenge: | Simultaneous machine translation begins to translate each source sentence before the source speaker has finished speaking, with applications to live and streaming scenarios. |
| Approach: | They propose a simultaneous translation system that learns an adaptive schedule with a neural machine translation model that attends over all source tokens read thus far. |
| Outcome: | The proposed system can achieve latency-quality trade-offs favorable to a proposed wait-k strategy for many latency values. |
Learning Optimal Policy for Simultaneous Machine Translation via Binary Search (2023.acl-long)
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| Challenge: | Simultaneous machine translation model needs a precise translation policy to achieve good latency-quality trade-offs. |
| Approach: | They propose a method for building the optimal translation policy online via binary search by employing explicit supervision. |
| Outcome: | Experiments on four translation tasks show that the proposed method exceeds strong baselines across all latency scenarios. |
Better Simultaneous Translation with Monotonic Knowledge Distillation (2023.acl-long)
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| Challenge: | Existing methods to train offline MT models require generating target tokens before source sentence is fully consumed. |
| Approach: | They propose a method that leverages traditional translation models as teachers to generate monotonic yet accurate reference translations for sequence-level knowledge distillation. |
| Outcome: | The proposed approach improves on strong baselines and on a monotonic version of the WMT15 De-En test set. |
Wait-info Policy: Balancing Source and Target at Information Level for Simultaneous Machine Translation (2022.findings-emnlp)
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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. |
| Approach: | They propose a Wait-info Policy to balance source and target at the information level . they quantify the amount of info contained in each token and compare it with previous outputs . |
| Outcome: | The proposed method outperforms baselines under and achieves better balance . it is based on comparisons between the total info of previous target outputs and received source inputs . |
Turning Fixed to Adaptive: Integrating Post-Evaluation into Simultaneous Machine Translation (2022.findings-emnlp)
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| Challenge: | Existing methods to perform adaptive and fixed translations lack evaluation before taking actions. |
| Approach: | They propose a method to perform adaptive translation policy via post-evaluation into fixed policy . their method evaluates rationality of next action by measuring change in source content . |
| Outcome: | The proposed method exceeds strong baselines under all latency. |
Modeling Dual Read/Write Paths for Simultaneous Machine Translation (2022.acl-long)
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| Challenge: | Simultaneous machine translation (SiMT) outputs translation while reading source sentence . existing methods do not direct the read/write path, resulting in poor performance . |
| Approach: | They propose a method which introduces duality constraints to direct the read/write path . they propose to map the read path in two SiMT models to satisfy duality constraint . |
| Outcome: | Experiments on En-Vi and De-En tasks show that the proposed method outperforms baselines under all latency. |
Simultaneous Machine Translation with Visual Context (2020.emnlp-main)
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| Challenge: | Simultaneous machine translation (SiMT) aims to reproduce human interpretation, where an interpreter translates spoken utterances as they are produced. |
| Approach: | They propose to add visual context to siMT to compensate for the missing source context . they show visual-grounded models are much better than commonly used global features . |
| Outcome: | The proposed models reach up to 3 BLEU points improvement under low latency scenarios. |
Simultaneous Machine Translation with Tailored Reference (2023.findings-emnlp)
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| Challenge: | Existing SiMT models are trained using the same reference disregarding the varying amounts of available source information at different latency. |
| Approach: | They propose a method that provides tailored reference for the SiMT models trained at different latency by rephrasing ground-truth to the tailored reference. |
| Outcome: | The proposed method achieves state-of-the-art translation performance on three translation tasks. |
Gaussian Multi-head Attention for Simultaneous Machine Translation (2022.findings-acl)
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| Challenge: | Existing methods for siMT do not explicitly model the alignment to perform the control. |
| Approach: | They propose to model alignment and translation in a unified manner by Gaussian Multi-head Attention (GMA) they propose to integrate alignment-related priors into the translation model to determine final attention. |
| Outcome: | The proposed method outperforms strong baselines on trade-off between translation and latency. |
Anticipating Future with Large Language Model for Simultaneous Machine Translation (2025.naacl-long)
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Siqi Ouyang, Oleksii Hrinchuk, Zhehuai Chen, Vitaly Lavrukhin, Jagadeesh Balam, Lei Li, Boris Ginsburg
| Challenge: | Existing methods only use the partial utterance that has already arrived at the input and the generated hypothesis. |
| Approach: | They propose to use a large language model to predict future source words and opportunistically translate without introducing too much risk. |
| Outcome: | The proposed method outperforms baselines on four language directions and achieves the best translation quality-latency trade-off by up to 5 BLEU points at the same latency. |
Adaptive Policy with Wait-k Model for Simultaneous Translation (2023.emnlp-main)
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| Challenge: | Existing approaches to simultaneous machine translation require a robust read/write policy . a standalone multi-path wait-k model performs competitively with adaptive policies . |
| Approach: | They propose a more flexible approach by decoupling the adaptive policy model from the translation model. |
| Outcome: | The proposed approach outperforms baseline approaches in translation tasks. |
Non-autoregressive Streaming Transformer for Simultaneous Translation (2023.emnlp-main)
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| Challenge: | Simultaneous machine translation models are trained to strike a balance between latency and translation quality. |
| Approach: | They propose a non-autoregressive streaming Transformer which generates blank tokens and decodes repetitive tokens to adjust its READ/WRITE strategy flexibly. |
| Outcome: | The proposed model outperforms previous strong autoregressive models on various benchmarks on siMT. |
Multilingual Simultaneous Neural Machine Translation (2021.findings-acl)
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| Challenge: | Simultaneous machine translation (SIMT) involves translating source utterances to the target language in real-time before the speaker utterrance completes. |
| Approach: | They propose a multilingual approach to simultaneous machine translation where a single model simultaneously translates between multiple languages. |
| Outcome: | The proposed multilingual approach improves on two Germanic and three Romance languages and is on-par or better than the universal model trained for all languages. |
Reducing Position Bias in Simultaneous Machine Translation with Length-Aware Framework (2022.acl-long)
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| Challenge: | Existing methods for simultaneous machine translation (SiMT) are more challenging since the source sentence is always incomplete during translating. |
| Approach: | They propose a framework to reduce the position bias by bridging the structural gap between SiMT and full-sentence MT. |
| Outcome: | The proposed framework reduces the position bias by bridging the structural gap between SiMT and full-sentence MT. |
Universal Simultaneous Machine Translation with Mixture-of-Experts Wait-k Policy (2021.emnlp-main)
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| Challenge: | Existing methods for simultaneous machine translation require multiple models for different latency levels, resulting in large computational costs. |
| Approach: | They propose a universal SiMT model with Mixture-of-Experts Wait-k Policy to achieve the best translation quality under arbitrary latency with only one model. |
| Outcome: | The proposed model outperforms all the strong baselines under different latency levels including the state-of-the-art adaptive policy. |
DrFrattn: Directly Learn Adaptive Policy from Attention for Simultaneous Machine Translation (2025.emnlp-main)
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| Challenge: | Existing approaches to learn read/write policies from attention mechanism may compromise effectiveness of attention mechanism . |
| Approach: | They propose a method that directly learns adaptive policies from the attention mechanism . experimental results demonstrate that the method achieves an improved balance between translation accuracy and latency. |
| Outcome: | The proposed method achieves improved balance between translation accuracy and latency. |