Papers with SiMT
TransLLaMa: LLM-based Simultaneous Translation System (2024.findings-emnlp)
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| Challenge: | Decoder-only large language models have limited applications in simultaneous machine translation . naively translating each source word immediately results in compromised target quality . |
| Approach: | a study shows that a pre-trained open-source LLM can control input segmentation directly by generating a special "wait" token. |
| Outcome: | a new open-source model can control input segmentation directly by generating a special "wait" token. |
On the Hallucination in Simultaneous Machine Translation (2024.acl-short)
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| Challenge: | Currently, there are no studies which systematically analyze hallucination in SiMT. |
| Approach: | They conduct a comprehensive analysis of hallucination in simultaneous machine translation (SiMT) they find that halluciation is extremely severe, especially as latency increases . |
| Outcome: | The results show that it is possible to alleviate hallucination by decreasing the over usage of target-side information for SiMT. |
LLMs Are Zero-Shot Context-Aware Simultaneous Translators (2024.emnlp-main)
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| Challenge: | Existing SiMT systems operate on a sentence level, disregarding the context established by previous sentences or the broader context implied by previous words. |
| Approach: | They show that open-source LLMs perform on par with or better than some state-of-the-art baselines in simultaneous machine translation tasks, zero-shot. |
| Outcome: | The proposed models perform on par with or better than state-of-the-art baselines in simultaneous machine translation tasks, zero-shot. |
PsFuture: A Pseudo-Future-based Zero-Shot Adaptive Policy for Simultaneous Machine Translation (2024.emnlp-main)
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| Challenge: | Simultaneous machine translation (SiMT) requires target tokens to be generated in real-time as streaming source tokens are consumed. |
| Approach: | They propose a zero-shot adaptive read/write policy for siMT that generates target tokens concurrently as streaming source tokens are consumed. |
| Outcome: | The proposed policy achieves performance on par with strong baselines and the P2F method can further enhance performance. |
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. |
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. |
Exploiting Multimodal Reinforcement Learning for Simultaneous Machine Translation (2021.eacl-main)
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| Challenge: | Existing studies on multimodality in simultaneous machine translation have highlighted the challenges for the agent to maintain good translation quality while learning an optimal translation path. |
| Approach: | They propose a multimodal approach to simultaneous machine translation using reinforcement learning with strategies to integrate visual and textual information in both the agent and the environment. |
| Outcome: | The proposed multimodal approach improves translation quality while keeping latency low while providing visual cues. |
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. |
Investigating Hallucinations in Simultaneous Machine Translation: Knowledge Distillation Solution and Components Analysis (2025.naacl-long)
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| Challenge: | Existing methods to mitigate hallucinations in siMT generate fluency but unfaithful translation. |
| Approach: | They propose a method that utilizes the OMT model to mitigate hallucinations in SiMT. |
| Outcome: | The proposed method reduces hallucinations and improves the SiMT performance. |
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. |
Decoder-only Streaming Transformer for Simultaneous Translation (2024.acl-long)
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| Challenge: | Existing methods for siMT focus on the Encoder-Decoder architecture, but there are limitations in training and inference. |
| Approach: | They propose a model that generates translation while reading source tokens . they propose Streaming Self-Attention mechanism tailored for the Decoder-only architecture . |
| Outcome: | The proposed model achieves state-of-the-art performance on three translation tasks. |
Self-Modifying State Modeling for Simultaneous Machine Translation (2024.acl-long)
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| Challenge: | Existing methods for simultaneous machine translation fail to optimize the policy . existing methods require building a decision path to learn the policy, but they cannot explore all potential paths . |
| Approach: | They propose a new training paradigm that uses a read/write policy to optimize the policy . existing methods usually require building a decision path to learn a suitable policy a user makes . |
| Outcome: | The proposed model outperforms strong baselines and allows offline models to acquire SiMT ability with fine-tuning. |
It Is Not As Good As You Think! Evaluating Simultaneous Machine Translation on Interpretation Data (2021.emnlp-main)
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| Challenge: | Existing siMT systems are trained and evaluated on offline translations . however, evaluation gap remains notable, calling for constructing large-scale interpretation corpora . |
| Approach: | They propose a translation-to-interpretation transfer method which converts offline translations into interpretation-style data. |
| Outcome: | The proposed interpretation test set shows that SiMT models improve on translation vs interpretation data. |
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. |
Redefining Machine Simultaneous Interpretation: From Incremental Translation to Human-Like Strategies (2026.findings-acl)
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| Challenge: | Simultaneous machine translation requires high-quality translations under strict real-time constraints. |
| Approach: | They extend the action space of simultaneous machine translation with four adaptive actions . they adapt these actions in a large language model framework and construct training references . |
| Outcome: | The proposed framework improves semantic metrics and achieves lower delay compared to reference translations and salami-based baselines. |
Context Consistency between Training and Inference in Simultaneous Machine Translation (2024.acl-long)
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| Challenge: | Simultaneous machine translation (SiMT) aims to yield a partial translation with a monotonically growing source-side context. |
| Approach: | They propose a training approach that encourages consistent context usage between training and inference by optimizing translation quality and latency as bi-objectives and exposing the predictions to the model during the training. |
| Outcome: | The proposed system outperforms existing SiMT systems with context inconsistency for the first time. |
SeqPO-SiMT: Sequential Policy Optimization for Simultaneous Machine Translation (2025.findings-acl)
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| Challenge: | SeqPO-SiMT is a new policy optimization framework for simultaneous machine translation that combines a tailored reward with a single step task. |
| Approach: | They propose a new policy optimization framework that defines the simultaneous machine translation task as a sequential decision making problem with a tailored reward. |
| Outcome: | The proposed framework outperforms the supervised fine-tuning model by 1.13 points while reducing the Average Lagging by 6.17 in the NEWSTEST2021 En Zh dataset. |
LLMs Can Achieve High-quality Simultaneous Machine Translation as Efficiently as Offline (2025.findings-acl)
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| Challenge: | Large language models perform well in offline machine translation when the complete source sentence is provided . however, in many real scenarios, the source tokens arrive in a streaming manner and simultaneous machine translation is required . |
| Approach: | They propose a new paradigm that includes constructing supervised fine-tuning data for simultaneous machine translation (SiMT) to achieve SiMT, source and target tokens are rearranged into interleaved sequences, separated by special tokens according to varying latency requirements. |
| Outcome: | The proposed approach achieves state-of-the-art performance across various SiMT benchmarks and evaluation metrics while maintaining efficient auto-regressive decoding. |
Enhanced Simultaneous Machine Translation with Word-level Policies (2023.findings-emnlp)
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| Challenge: | Existing studies assume that operations are carried out at the subword level . a novel policy dictates whether to READ or WRITE at each step of the translation process . |
| Approach: | They propose a method to boost SiMT models using language models to address subword disparity . they propose implementing a word-level policy that dictates whether to READ or WRITE . |
| Outcome: | The proposed policy improves the performance of SiMT models by boosting them with language models . the proposed policy plays a vital role in addressing the subword disparity between LMs and SiMT systems. |
NAIST-SIC-Aligned: An Aligned English-Japanese Simultaneous Interpretation Corpus (2024.lrec-main)
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| Challenge: | Simultaneous interpretation data is a task where an utterance is translated in real-time. |
| Approach: | They propose to use an automatically-aligned parallel English-Japanese SI dataset to make it suitable for model training. |
| Outcome: | The proposed model improves translation quality and latency over baselines. |