Summary of R-bi: Regularized Batched Inputs Enhance Incremental Decoding Framework For Low-latency Simultaneous Speech Translation, by Jiaxin Guo et al.
R-BI: Regularized Batched Inputs enhance Incremental Decoding Framework for Low-Latency Simultaneous Speech Translation
by Jiaxin Guo, Zhanglin Wu, Zongyao Li, Hengchao Shang, Daimeng Wei, Xiaoyu Chen, Zhiqiang Rao, Shaojun Li, Hao Yang
First submitted to arxiv on: 11 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Incremental Decoding framework enables the use of an offline model in a simultaneous setting without modifying the original model, making it suitable for Low-Latency Simultaneous Speech Translation. However, this framework may introduce errors when the system outputs from incomplete input. To reduce these output errors, several strategies such as Hold-n, LA-n, and SP-n can be employed, but the hyper-parameter n needs to be carefully selected for optimal performance. The authors propose a new adaptable and efficient policy named “Regularized Batched Inputs” that enhances input diversity to mitigate output errors. They suggest particular regularization techniques for both end-to-end and cascade systems. Experiments on IWSLT Simultaneous Speech Translation (SimulST) tasks demonstrate that their approach achieves low latency while maintaining no more than 2 BLEU points loss compared to offline systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The Incremental Decoding framework helps translate speech in real-time without changing the original model. However, it can make mistakes when not enough information is given. To fix this, you need to choose the right number n for certain strategies. The authors also suggest a new way called “Regularized Batched Inputs” that makes input data more diverse and accurate. They tested their method on simultaneous speech translation tasks and showed that it can do this quickly while still being as good as older systems. |
Keywords
» Artificial intelligence » Bleu » Regularization » Translation
