no code implementations • EAMT 2022 • Toms Bergmanis, Marcis Pinnis, Roberts Rozis, Jānis Šlapiņš, Valters Šics, Berta Bernāne, Guntars Pužulis, Endijs Titomers, Andre Tättar, Taido Purason, Hele-Andra Kuulmets, Agnes Luhtaru, Liisa Rätsep, Maali Tars, Annika Laumets-Tättar, Mark Fishel
We present the MTee project - a research initiative funded via an Estonian public procurement to develop machine translation technology that is open-source and free of charge.
no code implementations • 5 Apr 2024 • Hele-Andra Kuulmets, Taido Purason, Agnes Luhtaru, Mark Fishel
This paper explores cost-efficient methods to adapt pretrained Large Language Models (LLMs) to new lower-resource languages, with a specific focus on Estonian.
no code implementations • 8 Mar 2024 • Agnes Luhtaru, Taido Purason, Martin Vainikko, Maksym Del, Mark Fishel
This study explores enhancing grammatical error correction (GEC) through artificial error generation (AEG) using language models (LMs).
Ranked #1 on Grammatical Error Correction on UA-GEC
no code implementations • 18 Feb 2024 • Agnes Luhtaru, Martin Vainikko, Krista Liin, Kais Allkivi-Metsoja, Jaagup Kippar, Pille Eslon, Mark Fishel
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.
no code implementations • 27 Mar 2019 • Elizaveta Korotkova, Agnes Luhtaru, Maksym Del, Krista Liin, Daiga Deksne, Mark Fishel
Both grammatical error correction and text style transfer can be viewed as monolingual sequence-to-sequence transformation tasks, but the scarcity of directly annotated data for either task makes them unfeasible for most languages.