Wait-info Policy: Balancing Source and Target at Information Level for Simultaneous Machine Translation (2022.findings-emnlp)
Copied to clipboard
| 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 . |
Similar Papers
Information-Transport-based Policy for Simultaneous Translation (2022.emnlp-main)
Copied to clipboard
| 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. |
Adaptive Policy with Wait-k Model for Simultaneous Translation (2023.emnlp-main)
Copied to clipboard
| 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. |
Universal Simultaneous Machine Translation with Mixture-of-Experts Wait-k Policy (2021.emnlp-main)
Copied to clipboard
| 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. |
Turning Fixed to Adaptive: Integrating Post-Evaluation into Simultaneous Machine Translation (2022.findings-emnlp)
Copied to clipboard
| 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. |
Gaussian Multi-head Attention for Simultaneous Machine Translation (2022.findings-acl)
Copied to clipboard
| 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. |
Learning Optimal Policy for Simultaneous Machine Translation via Binary Search (2023.acl-long)
Copied to clipboard
| 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. |
DrFrattn: Directly Learn Adaptive Policy from Attention for Simultaneous Machine Translation (2025.emnlp-main)
Copied to clipboard
| 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. |
Enhanced Simultaneous Machine Translation with Word-level Policies (2023.findings-emnlp)
Copied to clipboard
| 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. |
Reducing Position Bias in Simultaneous Machine Translation with Length-Aware Framework (2022.acl-long)
Copied to clipboard
| 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. |
SimulMT to SimulST: Adapting Simultaneous Text Translation to End-to-End Simultaneous Speech Translation (2020.aacl-main)
Copied to clipboard
| Challenge: | Using end-to-end Simultaneous text translation, we adapt wait-k and monotonic multihead attention to end- to-end simultaneous speech translation. |
| Approach: | They propose to combine a fixed and flexible pre-decision module with fixed and flexibility policies to adapt simultaneous text translation methods such as wait-k and monotonic multihead attention to end-to-end simultaneous speech translation. |
| Outcome: | The proposed method can generate translations with maximum quality and minimal latency, targeting video caption translations and real-time language interpreter. |