AAdaM at SemEval-2024 Task 1: Augmentation and Adaptation for Multilingual Semantic Textual Relatedness
<|reference_start|>AAdaM at SemEval-2024 Task 1: Augmentation and Adaptation for Multilingual Semantic Textual Relatedness: 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).<|reference_end|>
@article{zhang2024aadam,
    title={AAdaM at SemEval-2024 Task 1: Augmentation and Adaptation for
  Multilingual Semantic Textual Relatedness},
    author={Miaoran Zhang, Mingyang Wang, Jesujoba O. Alabi, Dietrich Klakow},
    journal={arXiv preprint arXiv:2404.01490},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.01490},
    primaryClass={cs.CL}
}