Efficient Adaptive Convolutional Model Based on Label Embedding for Text Classification Using Low Resource Languages

Agbesi, Victor Kwaku; Chen, Wenyu; Ameneshewa, Abush S.; Odame, Emmanuel; Dumor, Koffi; Browne, Judith Ayekai

doi:10.1145/3596947.3596962

Proceedings of the 2023 7th International Conference on Intelligent Systems, Metaheuristics &Amp; Swarm Intelligence 2023

DOI: 10.1145/3596947.3596962

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Efficient Adaptive Convolutional Model Based on Label Embedding for Text Classification Using Low Resource Languages

Victor Kwaku Agbesi

Wenyu Chen

Abush S. Ameneshewa

et al.

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2023

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Pre-Trained Transformer-Based Models for Text Classification Using Low-Resourced Ewe Language

Agbesi,

Chen,

Yussif

et al. 2023

Systems

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Despite a few attempts to automatically crawl Ewe text from online news portals and magazines, the African Ewe language remains underdeveloped despite its rich morphology and complex "unique" structure. This is due to the poor quality, unbalanced, and religious-based nature of the crawled Ewe texts, thus making it challenging to preprocess and perform any NLP task with current transformer-based language models. In this study, we present a well-preprocessed Ewe dataset for low-resource text classification to the research community. Additionally, we have developed an Ewe-based word embedding to leverage the low-resource semantic representation. Finally, we have fine-tuned seven transformer-based models, namely BERT-based (cased and uncased), DistilBERT-based (cased and uncased), RoBERTa, DistilRoBERTa, and DeBERTa, using the preprocessed Ewe dataset that we have proposed. Extensive experiments indicate that the fine-tuned BERT-base-cased model outperforms all baseline models with an accuracy of 0.972, precision of 0.969, recall of 0.970, loss score of 0.021, and an F1-score of 0.970. This performance demonstrates the model’s ability to comprehend the low-resourced Ewe semantic representation compared to all other models, thus setting the fine-tuned BERT-based model as the benchmark for the proposed Ewe dataset.

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Pre-Trained Transformer-Based Models for Text Classification Using Low-Resourced Ewe Language

Agbesi,

Chen,

Yussif

et al. 2023

Systems

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Efficient Adaptive Convolutional Model Based on Label Embedding for Text Classification Using Low Resource Languages

Cited by 1 publication

References 13 publications

Pre-Trained Transformer-Based Models for Text Classification Using Low-Resourced Ewe Language

Pre-Trained Transformer-Based Models for Text Classification Using Low-Resourced Ewe Language

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