Work

SAAKTH at BEA 2026 Shared Task 1: L1-Aware English Vocabulary Difficulty Prediction with Hybrid Transformer and Psycholinguistic Features

Year: 2026

Type: conference-paper

Abstract: This paper presents team SAAKTH's system for the BEA 2026 Shared Task on Vocabulary Difficulty Prediction (Closed Track).We address the key challenge that English word difficulty is not fixed but varies with English learners' native language.Our approach combines a fine-tuned XLM-RoBERTa-large encoder with handcrafted psycholinguistic features engineered separately for each L1 group.These features are integrated via a shallow multilayer perceptron and optimized separately per L1, with five-seed ensembling and XGBoost-based blending for stability.Our system achieves RM-SEs of 0.997 (es), 1.002 (de), and 0.932 (cn) on the development set, improving 20-25% over the baseline.Results highlight the effectiveness of L1-aware modeling under limited data.

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

DOI: 10.18653/v1/2026.bea-1.69

Authors: Karthik Mattu, Adit Dhall, Arshad Naguru, Shubh Sehgal, Thejas Gowda, Hakyung Sung

Institution: Rochester Institute of Technology

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

OpenAlex ID: W7166837098