Summary of How to Learn in a Noisy World? Self-correcting the Real-world Data Noise in Machine Translation, by Yan Meng et al.
How to Learn in a Noisy World? Self-Correcting the Real-World Data Noise in Machine Translation
by Yan Meng, Di Wu, Christof Monz
First submitted to arxiv on: 2 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the issue of semantic misalignment in machine translation systems, which is a significant source of noise in training data. The authors introduce a process for simulating this type of misalignment and analyze its impact on machine translation. They find that widely used pre-filters are limited in detecting misalignment noise, highlighting the need for more sophisticated approaches. To address this issue, the authors propose self-correction, an approach that leverages the model’s ability to distinguish misaligned data from clean data at the token level. Experimental results show that self-correction significantly improves translation performance in both simulated and real-world noisy datasets across various tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is trying to improve how computers can translate languages like humans do. But, there’s a big problem: lots of bad information (noise) gets mixed into the training data. The authors of this paper figure out a way to create fake noise that looks just like the real thing. They then test how well machine translation works with this fake noise and find that most current methods aren’t very good at cleaning up the mess. To fix this, they suggest letting the computer learn from itself and correct its mistakes as it goes along. This new approach makes translations much better in both pretend and real-world situations. |
Keywords
» Artificial intelligence » Machine learning » Token » Translation
