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MaiNLP at SemEval-2024 Task 1: Analyzing Source Language Selection in Cross-Lingual Textual Relatedness

MCML Authors

Shijia Zhou

→ Group Barbara Plank
AI and Computational Linguistics

Barbara Plank

Prof. Dr.

Principal Investigator

AI and Computational Linguistics

Robert Litschko

→ Group Barbara Plank
AI and Computational Linguistics

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 from the same languages. For cross-lingual approach we developed a set of linguistics-inspired models trained with several task-specific strategies. We 1) utilize language vectors for selection of donor languages; 2) investigate the multi-source approach for training; 3) use transliteration of non-latin script to study impact of 'script gap'; 4) opt machine translation for data augmentation. We additionally compare the performance of XLM-RoBERTa and Furina with the same training strategy. Our submission achieved the first place in the C8 (Kinyarwanda) test.

inproceedings

SemEval @NAACL 2024

18th International Workshop on Semantic Evaluation at the Annual Conference of the North American Chapter of the Association for Computational Linguistics. Mexico City, Mexico, Jun 16-21, 2024.