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DOI: 10.14569/IJACSA.2023.01408108

Deep Learning-based Sentence Embeddings using BERT for Textual Entailment

Author 1: Mohammed Alsuhaibani

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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 8, 2023.

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Abstract: This study directly and thoroughly investigates the practicalities of utilizing sentence embeddings, derived from the foundations of deep learning, for textual entailment recognition, with a specific emphasis on the robust BERT model. As a cornerstone of our research, we incorporated the Stanford Natural Language Inference (SNLI) dataset. Our study emphasizes a meticulous analysis of BERT’s variable layers to ascertain the optimal layer for generating sentence embeddings that can effectively identify entailment. Our approach deviates from traditional methodologies, as we base our evaluation of entailment on the direct and simple comparison of sentence norms, subsequently highlighting the geometrical attributes of the embeddings. Experimental results revealed that the L2 norm of sentence embeddings, drawn specifically from BERT’s 7th layer, emerged superior in entailment detection compared to other setups.

Keywords: Textual entailment; deep learning; entailment detection; BERT; text processing; natural language processing systems

Mohammed Alsuhaibani, “Deep Learning-based Sentence Embeddings using BERT for Textual Entailment” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.01408108

@article{Alsuhaibani2023,
title = {Deep Learning-based Sentence Embeddings using BERT for Textual Entailment},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.01408108},
url = {http://dx.doi.org/10.14569/IJACSA.2023.01408108},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Mohammed Alsuhaibani}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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