Grammatikfehlerkorrektur mit Deep Reinforcement Learning

Raj Kumar RANA

Diplomová práce

Abstract:

Reinforcement Learning (RL) method was used to fine-tune a sequence-to-label model for the Grammar Error Correction (GEC) task. Required components of RL like the environment, reward function and policy gradient algorithm were implemented. An action-search algorithm was implemented to mitigate the training instability due to the high dimension of the state and action spaces. The action-search algorithm used the world model to guide the policy with action selection. The results of our models trained with Supervised Learning (SL) and RL were compared to other GEC systems on three GEC benchmarks.

Klíčová slova

Deep RL, Grammar Error Correction, NLP

Jazyk práce: čeština

Datum vytvoření / odevzdání či podání práce: 6. 2. 2023

Obhajoba závěrečné práce

• Vedoucí: prof. Dr. Andreas Fischer

JIHOČESKÁ UNIVERZITA V ČESKÝCH BUDĚJOVICÍCH

Přírodovědecká fakulta

Magisterský studijní program / obor:
Artificial Intelligence and Data Science / Artificial Intelligence and Data Science