Semantic textual similarity for modern standard and dialectal Arabic using transfer learning.

Autor: Al Sulaiman M; Department of Computer Engineering, College of Computer and Information Sciences (CCIS), King Saud University, Riyadh, Saudi Arabia.; Center of Smart Robotics Research, College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia., Moussa AM; Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt., Abdou S; Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt., Elgibreen H; Information Technology Department, College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia.; Center of Smart Robotics Research, College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia.; Artificial Intelligence Center of Advance Studies (Thakaa), King Saud University, Riyadh, Saudi Arabia., Faisal M; Center of AI & Robotics, Kuwait College of Science and Technology (KCST), Kuwait City, Kuwait.; Center of Smart Robotics Research, College of Computer and Information Science, King Saud University, Riyadh, Saudi Arabia.; Artificial Intelligence Center of Advance Studies (Thakaa), King Saud University, Riyadh, Saudi Arabia., Rashwan M; Faculty of Engineering, Cairo University, Giza, Egypt.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2022 Aug 11; Vol. 17 (8), pp. e0272991. Date of Electronic Publication: 2022 Aug 11 (Print Publication: 2022).
DOI: 10.1371/journal.pone.0272991
Abstrakt: Semantic Textual Similarity (STS) is the task of identifying the semantic correlation between two sentences of the same or different languages. STS is an important task in natural language processing because it has many applications in different domains such as information retrieval, machine translation, plagiarism detection, document categorization, semantic search, and conversational systems. The availability of STS training and evaluation data resources for some languages such as English has led to good performance systems that achieve above 80% correlation with human judgment. Unfortunately, such required STS data resources are not available for many languages like Arabic. To overcome this challenge, this paper proposes three different approaches to generate effective STS Arabic models. The first one is based on evaluating the use of automatic machine translation for English STS data to Arabic to be used in fine-tuning. The second approach is based on the interleaving of Arabic models with English data resources. The third approach is based on fine-tuning the knowledge distillation-based models to boost their performance in Arabic using a proposed translated dataset. With very limited resources consisting of just a few hundred Arabic STS sentence pairs, we managed to achieve a score of 81% correlation, evaluated using the standard STS 2017 Arabic evaluation set. Also, we managed to extend the Arabic models to process two local dialects, Egyptian (EG) and Saudi Arabian (SA), with a correlation score of 77.5% for EG dialect and 76% for the SA dialect evaluated using dialectal conversion from the same standard STS 2017 Arabic set.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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