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We propose an ensemble model for predicting the lexical complexity of wordsand multiword expressions (MWEs). The model receives as input a sentence with atarget word or MWEand outputs its complexity score. Given that a key challengewith this task is the limited size of annotated data, our model relies onpretrained contextual representations from different state-of-the-arttransformer-based language models (i.e., BERT and RoBERTa), and on a variety oftraining methods for further enhancing model generalization androbustness:multi-step fine-tuning and multi-task learning, and adversarialtraining. Additionally, we propose to enrich contextual representations byadding hand-crafted features during training. Our model achieved competitiveresults and ranked among the top-10 systems in both sub-tasks.

OCHADAI-KYOTO at SemEval-2021 Task 1: Enhancing Model Generalization and Robustness for Lexical Complexity Prediction