We present LexComSpaL2, a novel corpus which can be employed to train personalised word-level difficulty classifiers for learners of Spanish as a foreign/second language (L2). The dataset contains 2,240 in-context target words with the corresponding difficulty judgements of 26 Dutch-speaking students who are learning Spanish as an L2, resulting in a total of 58,240 annotations. The target words are divided over 200 sentences from 4 different domains (economics, health, law, and migration) and have been selected based on their suitability to be included in L2 learning materials. As our annotation scheme, we use a customised version of the 5-point lexical complexity prediction scale (Shardlow et al., 2020), tailored to the vocabulary knowledge continuum (which ranges from no knowledge over receptive mastery to productive mastery; Schmitt, 2019). With LexComSpaL2, we aim to address the lack of relevant data for multi-category difficult prediction at word level for L2 learners of other languages than English.

This study presents a lexical simplification (LS) methodology for foreign language (FL) learning purposes, a barely explored area of automatic text simplification (TS). The method, targeted at Spanish as a foreign language (SFL), includes a customised complex word identification (CWI) classifier and generates substitutions based on masked language modelling. Performance is calculated on a custom dataset by means of a new, pedagogically-oriented evaluation. With 43% of the top simplifications being found suitable, the method shows potential for simplifying sentences to be used in FL learning activities. The evaluation also suggests that, though still crucial, meaning preservation is not always a prerequisite for successful LS. To arrive at grammatically correct and more idiomatic simplifications, future research could study the integration of association measures based on co-occurrence data.