MasonTigers at SemEval-2024 Task 1: An Ensemble Approach for Semantic Textual Relatedness
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910400077889536 |
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| author | Goswami, Dhiman Puspo, Sadiya Sayara Chowdhury Raihan, Md Nishat Emran, Al Nahian Bin Ganguly, Amrita Zampieri, Marcos |
| author_facet | Goswami, Dhiman Puspo, Sadiya Sayara Chowdhury Raihan, Md Nishat Emran, Al Nahian Bin Ganguly, Amrita Zampieri, Marcos |
| contents | This paper presents the MasonTigers entry to the SemEval-2024 Task 1 - Semantic Textual Relatedness. The task encompasses supervised (Track A), unsupervised (Track B), and cross-lingual (Track C) approaches across 14 different languages. MasonTigers stands out as one of the two teams who participated in all languages across the three tracks. Our approaches achieved rankings ranging from 11th to 21st in Track A, from 1st to 8th in Track B, and from 5th to 12th in Track C. Adhering to the task-specific constraints, our best performing approaches utilize ensemble of statistical machine learning approaches combined with language-specific BERT based models and sentence transformers. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2403_14990 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MasonTigers at SemEval-2024 Task 1: An Ensemble Approach for Semantic Textual Relatedness Goswami, Dhiman Puspo, Sadiya Sayara Chowdhury Raihan, Md Nishat Emran, Al Nahian Bin Ganguly, Amrita Zampieri, Marcos Computation and Language This paper presents the MasonTigers entry to the SemEval-2024 Task 1 - Semantic Textual Relatedness. The task encompasses supervised (Track A), unsupervised (Track B), and cross-lingual (Track C) approaches across 14 different languages. MasonTigers stands out as one of the two teams who participated in all languages across the three tracks. Our approaches achieved rankings ranging from 11th to 21st in Track A, from 1st to 8th in Track B, and from 5th to 12th in Track C. Adhering to the task-specific constraints, our best performing approaches utilize ensemble of statistical machine learning approaches combined with language-specific BERT based models and sentence transformers. |
| title | MasonTigers at SemEval-2024 Task 1: An Ensemble Approach for Semantic Textual Relatedness |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2403.14990 |