The present study introduces a new, open-access dataset on acceptability ratings of Korean clausal constructions at the morphosyntax–semantics interface (dative, passive, and negative polarity item). The dataset comprises (i) linguistically controlled sentence materials, (ii) ratings from targeted adult populations (individuals in their 20s), and (iii) parallel ratings from GPT variants (including ChatGPT). Alongside the release, we assess the alignment between GPT- and human-derived ratings to probe the extent to which GPT architectures can approximate patterns of human sentence comprehension.

The present study extends recent work on Universal Dependencies annotations for second-language (L2) Korean by introducing a semi-automated framework that identifies morphosyntactic constructions from XPOS sequences and aligns those constructions with corresponding UPOS categories. We also broaden the existing L2-Korean corpus by annotating 2,998 new sentences from argumentative essays. To evaluate the impact of XPOS-UPOS alignments, we fine-tune L2-Korean morphosyntactic analysis models on datasets both with and without these alignments, using two NLP toolkits. Our results indicate that the aligned dataset not only improves consistency across annotation layers but also enhances morphosyntactic tagging and dependency-parsing accuracy, particularly in cases of limited annotated data.