Examining Large Language Models' form-meaning mappings of information structure constructions in Mandarin Chinese

Open Access
Authors
Publication date 2026
Host editors
  • Claire Bonial
  • Yevgeni Berzak
Book title The 30th Conference on Computational Natural Language Learning (CoNLL 2026) : proceedings of the conference
Book subtitle CoNNL 2026 : July 3-4, 2026
ISBN (electronic)
  • 9798891764101
Event 30th Conference on Computational Natural Language Learning
Pages (from-to) 613-625
Publisher Kerrville, TX: Association for Computational Linguistics
Organisations
  • Interfacultary Research - Institute for Logic, Language and Computation (ILLC)
Abstract
Construction Grammar (CxG) knowledge in language models has been extensively studied for English, but remains underexplored in other languages. In Mandarin Chinese, the ba (把, disposal) and bei (被, passive) constructions are widely used for managing information structure. They foreground topical elements (information structure) and encode systematic form-meaning mappings (CxG), particularly with respect to the semantic role of the object. We probe language models’ linguistic competence with these constructions using minimal pairs, constructing a new minimal-pair dataset comprising seven paradigms that target both syntactic constraints and verb–construction compatibility. Our results show that it remains a challenge for many models to capture the form-meaning mappings underlying the ba construction, although they achieve high accuracy on paradigms driven by surface syntactic cues.
Document type Conference contribution
Language English
Published at
Other links
Downloads
2026.conll-main.37 (Final published version)
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