Abstract
This paper describes our submission to the SemEval-2021 shared task on Lexical Complexity Prediction. We approached it as a regression problem and present an ensemble combining four systems, one feature-based and three neural with fine-tuning, frequency pre-training and multi-task learning, achieving Pearson scores of 0.8264 and 0.7556 on the trial and test sets respectively (sub-task 1). We further present our analysis of the results and discuss our findings.- Anthology ID:
- 2021.semeval-1.74
- Volume:
- Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021)
- Month:
- August
- Year:
- 2021
- Address:
- Online
- Editors:
- Alexis Palmer, Nathan Schneider, Natalie Schluter, Guy Emerson, Aurelie Herbelot, Xiaodan Zhu
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 590–597
- Language:
- URL:
- https://aclanthology.org/2021.semeval-1.74
- DOI:
- 10.18653/v1/2021.semeval-1.74
- Cite (ACL):
- Zheng Yuan, Gladys Tyen, and David Strohmaier. 2021. Cambridge at SemEval-2021 Task 1: An Ensemble of Feature-Based and Neural Models for Lexical Complexity Prediction. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 590–597, Online. Association for Computational Linguistics.
- Cite (Informal):
- Cambridge at SemEval-2021 Task 1: An Ensemble of Feature-Based and Neural Models for Lexical Complexity Prediction (Yuan et al., SemEval 2021)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2021.semeval-1.74.pdf