IITK at SemEval-2024 Task 1: Contrastive Learning and Autoencoders for Semantic Textual Relatedness in Multilingual Texts
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Abstract (arXiv)
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.
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References · 9
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lightercited by 4,243
- 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
- 2019How multilingual is Multilingual BERT?cited by 642
- 2020Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillationcited by 591
- 2020CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Datacited by 24
- 2024SemRel2024: A Collection of Semantic Textual Relatedness Datasets for 13 Languagescited by 16
- 2023L3Cube-IndicSBERT: A simple approach for learning cross-lingual sentence representations using multilingual BERTcited by 15
- 2021IITK@LCP at SemEval 2021 Task 1: Classification for Lexical Complexity Regression Taskcited by 3
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