<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Knowing that language is often used as a classifier of human intelligence and the development of systems that understand human language remains a challenge all the time (Kryeziu &amp; Shehu, 2022). Natural Language Processing is a very active field of study, where transformers have a key role. Transformers function based on neural networks and they are increasingly showing promising results. One of the first major contributions to transfer learning in Natural Language Processing was the use of pre-trained word embeddings in 2010 (Joseph, Lev, &amp; Yoshua, 2010). Pre-trained models like ELMo (Matthew, et al., 2018) and BERT (Delvin, et al., 2019) are trained on large corpora of unlabeled text and as a result learning from text representations has achieved good performance on many of the underlying tasks on datasets from different domains. Pre-training in the language model has proven that there has been an improvement in some aspects of natural language processing, based on the paper (Dai &amp; Le, 2015). In present paper, we will pre-train BERT on the task of Masked Language Modeling (MLM) with the Albanian language dataset (alb_dataset) that we have created for this purpose (Kryeziu et al., 2022). We will compare two approaches: training of BERT using the available OSCAR dataset and using our alb_dataset that we have collected. The paper shows some discrepancies during training, especially while evaluating the performance of the model.</p>
</abstract>

Knowing that language is often used as a classifier of human intelligence and the development of systems that understand human language remains a challenge all the time (Kryeziu & Shehu, 2022). Natural Language Processing is a very active field of study, where transformers have a key role. Transformers function based on neural networks and they are increasingly showing promising results. One of the first major contributions to transfer learning in Natural Language Processing was the use of pre-trained word embeddings in 2010 (Joseph, Lev, & Yoshua, 2010). Pre-trained models like ELMo (Matthew, et al., 2018) and BERT (Delvin, et al., 2019) are trained on large corpora of unlabeled text and as a result learning from text representations has achieved good performance on many of the underlying tasks on datasets from different domains. Pre-training in the language model has proven that there has been an improvement in some aspects of natural language processing, based on the paper (Dai & Le, 2015). In present paper, we will pre-train BERT on the task of Masked Language Modeling (MLM) with the Albanian language dataset (alb_dataset) that we have created for this purpose (Kryeziu et al., 2022). We will compare two approaches: training of BERT using the available OSCAR dataset and using our alb_dataset that we have collected. The paper shows some discrepancies during training, especially while evaluating the performance of the model.

Pre-Training MLM Using Bert for the Albanian Language

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This work is licensed under the Creative Commons Attribution 4.0 International License.

{"article-title":"Pre-Training MLM Using Bert for the Albanian Language"}

Knowing that language is often used as a classifier of human intelligence and the development of systems that understand human language remains a challenge all...

Pre-Training MLM Using Bert for the Albanian Language

Pubblicato online: 28 giu 2023

Pagine: 52 - 62

DOI: https://doi.org/10.2478/seeur-2023-0035

Parole chiave
NLP, transformers, pre-training, BERT

© 2023 Labehat Kryeziu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Pre-Training MLM Using Bert for the Albanian Language

Pubblicato online: 28 giu 2023

Pagine: 52 - 62

DOI: https://doi.org/10.2478/seeur-2023-0035

Parole chiaveNLP, transformers, pre-training, BERT

© 2023 Labehat Kryeziu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
NLP, transformers, pre-training, BERT