Papers with SiMT models
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 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. |
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. |
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. |
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. |
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. |