@inproceedings{zhang2024aadamsemeval2024task1,
title = {AAdaM at SemEval-2024 Task 1: Augmentation and Adaptation for Multilingual Semantic Textual Relatedness},
author = {Miaoran Zhang and Wang Mingyang and Alabi Jesujoba  and Dietrich Klakow},
editor = {Atul Kr. Ojha and A. Seza Doğru{\"o}z and Harish Tayyar Madabushi and Giovanni Da San Martino and Sara Rosenthal and Aiala Ros{\'a}},
url = {https://aclanthology.org/2024.semeval-1.114},
doi = {https://doi.org/10.18653/v1/2024.semeval-1.114},
year = {2024},
date = {2024},
booktitle = {Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)},
pages = {800-810},
publisher = {Association for Computational Linguistics},
address = {Mexico City, Mexico},
abstract = {This paper presents our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languages. The shared task aims at measuring the semantic textual relatedness between pairs of sentences, with a focus on a range of under-represented languages. In this work, we propose using machine translation for data augmentation to address the low-resource challenge of limited training data. Moreover, we apply task-adaptive pre-training on unlabeled task data to bridge the gap between pre-training and task adaptation. For model training, we investigate both full fine-tuning and adapter-based tuning, and adopt the adapter framework for effective zero-shot cross-lingual transfer. We achieve competitive results in the shared task: our system performs the best among all ranked teams in both subtask A (supervised learning) and subtask C (cross-lingual transfer).},
pubstate = {published},
type = {inproceedings}
}