Comparative Robustness of XLM-R and Furina in Source Language Selection for Cross-Lingual Textual Relatedness

Abstract

Abstract: This paper presents our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness (STR), on Track C: Cross-lingual. The task aims to detect semantic relatedness of two sentences in a given target language without access to direct supervision (i.e. zero-shot cross-lingual transfer). To this end, we focus on different source language selection strategies on two different pre-trained languages models: XLM-R and Furina. We experiment with 1) single-source transfer and select source languages based on typological similarity, 2) augmenting English training data with the two nearest-

Research Question

What is the comparative robustness of XLM-R and Furina models when selecting source languages with varying degrees of syntactic divergence for cross-lingual textual relatedness tasks?

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