Summary of Autocorrect For Estonian Texts: Final Report From Project Ektb25, by Agnes Luhtaru et al.
Autocorrect for Estonian texts: final report from project EKTB25
by Agnes Luhtaru, Martin Vainikko, Krista Liin, Kais Allkivi-Metsoja, Jaagup Kippar, Pille Eslon, Mark Fishel
First submitted to arxiv on: 18 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 proposed project aimed to develop spelling and grammar correction tools for the Estonian language. The main challenge was the scarcity of available error correction data required for model training and testing. To address this, the researchers annotated more correction data, utilized transfer-learning by retraining machine learning models from other tasks, compared their developed method with alternatives like large language models, and created automatic evaluation metrics to assess the accuracy and yield of corrections by error category. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The project aimed to create spelling and grammar correction tools for Estonian. The main problem was that there wasn’t much data available for training the models. To fix this, they made more data, used machine learning models from other tasks, compared their work with other methods, and created a way to measure how well each method works. |
Keywords
» Artificial intelligence » Machine learning » Transfer learning
