Abstract
This paper presents GMU’s submission to the Multilingual Lexical Simplification Pipeline (MLSP) shared task at the BEA workshop 2024. The task includes Lexical Complexity Prediction (LCP) and Lexical Simplification (LS) sub-tasks across 10 languages. Our submissions achieved rankings ranging from 1st to 5th in LCP and from 1st to 3rd in LS. Our best performing approach for LCP is a weighted ensemble based on Pearson correlation of language specific transformer models trained on all languages combined. For LS, GPT4-turbo zero-shot prompting achieved the best performance.- Anthology ID:
- 2024.bea-1.57
- Volume:
- Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Ekaterina Kochmar, Marie Bexte, Jill Burstein, Andrea Horbach, Ronja Laarmann-Quante, Anaïs Tack, Victoria Yaneva, Zheng Yuan
- Venue:
- BEA
- SIG:
- SIGEDU
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 627–634
- Language:
- URL:
- https://aclanthology.org/2024.bea-1.57
- DOI:
- Cite (ACL):
- Dhiman Goswami, Kai North, and Marcos Zampieri. 2024. GMU at MLSP 2024: Multilingual Lexical Simplification with Transformer Models. In Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024), pages 627–634, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- GMU at MLSP 2024: Multilingual Lexical Simplification with Transformer Models (Goswami et al., BEA 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.bea-1.57.pdf