Summary of Robust Guidance For Unsupervised Data Selection: Capturing Perplexing Named Entities For Domain-specific Machine Translation, by Seunghyun Ji et al.
Robust Guidance for Unsupervised Data Selection: Capturing Perplexing Named Entities for Domain-Specific Machine Translation
by Seunghyun Ji, Hagai Raja Sinulingga, Darongsae Kwon
First submitted to arxiv on: 29 Feb 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 The paper introduces a novel unsupervised data selection method called “Capturing Perplexing Named Entities” that leverages maximum inference entropy in translated named entities as a metric for selection. This approach addresses the challenge of identifying training-efficient data within an unsupervised setting, which is particularly important for neural machine translation when dealing with low-resourced data. The proposed method is tested on the “Korean-English Parallel Corpus of Specialized Domains” and outperforms existing methods in identifying robust guidance for training-efficient data across different domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a big problem in machine learning called low-resourced data, which makes it hard to translate languages. The solution involves finding the best data to use without any labels or help from experts. The researchers came up with a new way to do this by looking at how well named entities are translated. They tested their method on some Korean-English language data and showed that it works better than other methods. |
Keywords
» Artificial intelligence » Inference » Machine learning » Translation » Unsupervised
