SubmissionNumber#=%=#212
FinalPaperTitle#=%=#PEAR at SemEval-2024 Task 1: Pair Encoding with Augmented Re-sampling for Semantic Textual Relatedness
ShortPaperTitle#=%=#
NumberOfPages#=%=#7
CopyrightSigned#=%=#Tollef Emil Jørgensen
JobTitle#==#
Organization#==#
Abstract#==#This paper describes a system submitted to the supervised track (Track A) at SemEval-24: Semantic Textual Relatedness for African and Asian Languages. Challenged with datasets of varying sizes, some as small as 800 samples, we observe that the PEAR system, using smaller pre-trained masked language models to process sentence pairs (Pair Encoding), results in models that efficiently adapt to the task.
In addition to the simplistic modeling approach, we experiment with hyperparameter optimization and data expansion from the provided training sets using multilingual bi-encoders, sampling a dynamic number of nearest neighbors (Augmented Re-sampling). The final models are lightweight, allowing fast experimentation and integration of new languages.
Author{1}{Firstname}#=%=#Tollef Emil
Author{1}{Lastname}#=%=#Jørgensen
Author{1}{Username}#=%=#ph10m
Author{1}{Email}#=%=#tollef.jorgensen@ntnu.no
Author{1}{Affiliation}#=%=#Norwegian University of Science and Technology

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