Cambridge at SemEval-2021 Task 1: An Ensemble of Feature-Based and Neural Models for Lexical Complexity Prediction

Zheng Yuan, Gladys Tyen, David Strohmaier


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
Venue:
SemEval
SIGs:
SIGLEX | SIGSEM
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
Bibkey:
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)
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PDF:
https://aclanthology.org/2021.semeval-1.74.pdf