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Dergiler / Balkan Journal of Electrical and Computer Engineering / 2024 / Cilt: 12 Sayı: 4

Comparison of Transformer-Based Turkish Models for Question-Answering Task

Mehmet Arzu(FIRAT UNIVERSITY, FACULTY OF ENGINEERING)0000-0001-6610-2788
Murat Aydoğan(FIRAT ÜNİVERSİTESİ, TEKNOLOJİ FAKÜLTESİ)0000-0002-6876-6454
Sayfa
387–393
Yayın Tarihi
2025-01-07
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

According to the findings, it was observed that higher Exact Match (EM) and F1 scores were obtained in models with case sensitivity; the best performance was obtained with 63.99 EM and 80.84 F1 scores in the BERTurk (Cased, 128k) model.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Question-answering systems facilitate information access processes by providing fast and accurate answers to questions that users express in natural language.
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Temel Bulgular & Veri:Today, advances in Natural Language Processing (NLP) techniques increase the effectiveness of such systems and improve the user experience.
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Sonuç & Katkı:According to the findings, it was observed that higher Exact Match (EM) and F1 scores were obtained in models with case sensitivity; the best performance was obtained with 63.99 EM and 80.84 F1 scores in the BERTurk (Cased, 128k) model.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Natural Language Processing · Question-Answering System · Transformer

Abstract

Question-answering systems facilitate information access processes by providing fast and accurate answers to questions that users express in natural language. Today, advances in Natural Language Processing (NLP) techniques increase the effectiveness of such systems and improve the user experience. However, for these systems to work effectively, an accurate understanding of the structural properties of language is required. Traditional rule-based and knowledge retrieval-based systems are not able to analyze the contextual meaning of questions and texts deeply enough and therefore cannot produce satisfactory answers to complex questions. For this reason, Transformer-based models that can better capture the contextual and semantic integrity of the language have been developed. In this study, within the scope of the developed models, the performances of BERTurk, ELECTRA Turkish and DistilBERTurk models for Turkish question-answer tasks were compared by fine-tuning under the same hyperparameters and the results obtained were evaluated. According to the findings, it was observed that higher Exact Match (EM) and F1 scores were obtained in models with case sensitivity; the best performance was obtained with 63.99 EM and 80.84 F1 scores in the BERTurk (Cased, 128k) model.

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Bu Makaleye Atıf Yap

Arzu, M., Aydoğan, M. (2025). Comparison of Transformer-Based Turkish Models for Question-Answering Task. Balkan Journal of Electrical and Computer Engineering, Cilt: 12 Sayı: 4, 387–393. https://doi.org/10.17694/bajece.1576976

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Kaynakça (31)

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