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LITHME: Language in the Human-Machine Era
The LITHME COST Action brings together researchers from various fields of study focusing on language and technology.
Autocorrect for Estonian texts: final report from project EKTB25
To mitigate this, (1) we annotated more correction data for model training and testing, (2) we tested transfer-learning, i. e. retraining machine learning models created for other tasks, so as not to depend solely on correction data, (3) we compared the developed method and model with alternatives, including large language models.
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