Mitigating Learnerese Effects for CEFR Classification
Rricha Jalota | Peter Bourgonje | Jan Van Sas | Huiyan Huang
Proceedings of the 17th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2022)
The role of an author’s L1 in SLA can be challenging for automated CEFR classification, in that texts from different L1 groups may be too heterogeneous to combine them as training data. We experiment with recent debiasing approaches by attempting to devoid textual representations of L1 features. This results in a more homogeneous group when aggregating CEFR-annotated texts from different L1 groups, leading to better classification performance. Using iterative null-space projection, we marginally improve classification performance for a linear classifier by 1 point. An MLP (e.g. non-linear) classifier remains unaffected by this procedure. We discuss possible directions of future work to attempt to increase this performance gain.