mlsanityData to 7 Oct 2026

AAdaM at SemEval-2024 Task 1: Augmentation and Adaptation for Multilingual Semantic Textual Relatedness

arXiv 2404.01490 ↗First posted 1 Apr 2024cs.CLcited by 4

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Abstract (arXiv)

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).

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 30 of its 44 references that are in the map. First posted on arXiv 1 Apr 2024.

Builds on

30 of 44 references.

References · 30

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18 more
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  18. 2024SemEval-2024 Task 1: Semantic Textual Relatedness for African and Asian Languagescited by 7

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Cited by year

Citing papers per year: 2024: 1; 2025: 3.

4 papers.

  1. 2025Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Aheadcited by 15
  2. 2025Bring Your Own Knowledge: A Survey of Methods for LLM Knowledge Expansioncited by 8
  3. 2024How Transliterations Improve Crosslingual Alignmentcited by 6
  4. 2025AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Textcited by 4