Work

SATLab at BEA 2026 Shared Task 1: Predicting the Difficulty of English Words for Three L1 Learners Using Primarily Psycholinguistic Features

Year: 2026

Type: conference-paper

Abstract: This paper presents SATLab's participation in the BEA 2026 shared task on predicting the difficulty of English words for L2 learners.The proposed system uses features mainly derived from word frequency lists, lexical norms, and psychometric data, which are input into a gradient boosting decision tree model.It outperformed the Baseline system but performed significantly worse than the top-performing teams.Feature contributions to model performance are analysed using gain scores and Spearman rank correlations, and a brief analysis of the most significant errors is provided.

Source: Workshop on Innovative Use of NLP for Building Educational Applications

DOI: 10.18653/v1/2026.bea-1.66

Author: Yves Bestgen

Institutions: UCLouvain, KU Leuven

Language: English

Topic: Second Language Acquisition and Learning

Subfield: Developmental and Educational Psychology

Field: Psychology

Domain: Social Sciences

SDG: Quality education

Open Access status: hybrid

Funder: Fonds De La Recherche Scientifique - FNRS