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- W4313190929 abstract "Question answering is the task of predicting answers for questions based on a context paragraph. It has become especially important, as the large amounts of textual data available online requires not only gathering information but also the task of findings specific answers to specific questions. In this work, we present experiments evaluated on the XQuAD-ro question answering dataset that has been recently published based on the translation of the SQuAD dataset into Romanian. Our bestperforming model, Romanian fine-tuned BERT, achieves an F1 score of 0.80 and an EM score of 0.73. We show that fine-tuning the model with the addition of the Romanian translation slightly increases the evaluation metrics. Keywords and phrases: question answering, deep learning, Transformer, Romanian. " @default.
- W4313190929 created "2023-01-06" @default.
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- W4313190929 date "2022-07-03" @default.
- W4313190929 modified "2023-09-30" @default.
- W4313190929 title "Romanian Question Answering Using Transformer Based Neural Networks" @default.
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- W4313190929 doi "https://doi.org/10.24193/subbi.2022.1.03" @default.
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