AAdaM at SemEval-2024 Task 1: Augmentation and Adaptation for
  Multilingual Semantic Textual Relatedness
Miaoran Zhang Mingyang Wang Jesujoba O. Alabi Dietrich Klakow
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).