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

ABSTRACT: 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

SUBMITTER: Al Sulaiman M 

PROVIDER: S-EPMC9371328 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature