BERT2D: Two Dimensional Positional Embeddings for Efficient Turkish NLP

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AJRLQM3B3" target="_blank" >RIV/00216208:11320/25:JRLQM3B3 - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194891514&doi=10.1109%2fACCESS.2024.3407983&partnerID=40&md5=e13a78472733dd2230be25d4fbf75df7" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85194891514&doi=10.1109%2fACCESS.2024.3407983&partnerID=40&md5=e13a78472733dd2230be25d4fbf75df7</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ACCESS.2024.3407983" target="_blank" >10.1109/ACCESS.2024.3407983</a>

Result language
angličtina
Original language name
BERT2D: Two Dimensional Positional Embeddings for Efficient Turkish NLP
Original language description
This study addresses the challenge of improving the downstream performance of pretrained language models for morphologically rich languages, with a focus on Turkish. Traditional BERT models use one-dimensional absolute positional embeddings, which, while effective, have limitations when dealing with complex languages. We propose BERT2D, which is a novel BERT-based model that contributes to positional embedding systems. This approach introduces a dual embedding system that targets all the words and their subwords. Remarkably, this modification, coupled with whole word masking, resulted in a significant increase in performance despite a negligible increase in the parameters. Our experiments showed that BERT2D consistently outperformed the leading Turkish-focused BERT model, BERTurk, in terms of various performance metrics in text classification, token classification, and question-answering downstream tasks. For a fair comparison, we pretrained our BERT2D language model on the same dataset as that of BERTurk. The results demonstrate that two-dimensional positional embeddings can significantly improve the performance of encoder-only models in Turkish and other morphologically rich languages, suggesting a promising direction for future research in natural language processing. © 2013 IEEE.
Czech name
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Czech description
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Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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