IITK at SemEval-2024 Task 1: Contrastive Learning and Autoencoders for Semantic Textual Relatedness in Multilingual Texts
<|reference_start|>IITK at SemEval-2024 Task 1: Contrastive Learning and Autoencoders for Semantic Textual Relatedness in Multilingual Texts: This paper describes our system developed for the SemEval-2024 Task 1: Semantic Textual Relatedness. The challenge is focused on automatically detecting the degree of relatedness between pairs of sentences for 14 languages including both high and low-resource Asian and African languages. Our team participated in two subtasks consisting of Track A: supervised and Track B: unsupervised. This paper focuses on a BERT-based contrastive learning and similarity metric based approach primarily for the supervised track while exploring autoencoders for the unsupervised track. It also aims on the creation of a bigram relatedness corpus using negative sampling strategy, thereby producing refined word embeddings.<|reference_end|>
@article{basak2024iitk,
    title={IITK at SemEval-2024 Task 1: Contrastive Learning and Autoencoders for
  Semantic Textual Relatedness in Multilingual Texts},
    author={Udvas Basak and Rajarshi Dutta and Shivam Pandey and Ashutosh Modi},
    journal={arXiv preprint arXiv:2404.04513},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.04513},
    primaryClass={cs.CL cs.AI cs.LG}
}