Supervision versus Demonstration-Based In-Context Learning for Multiword Expression Classification
arXiv:2606.07479v1 Announce Type: cross Abstract: Turkish idiomatic light verb constructions (LVCs) are challenging for multiword expression processing because they often share the same surface form as fully literal verb-object combinations while functioning as a single, partially idiomatic predicate. We frame Turkish LVC detection as a binary classification task (literal meaning vs. idiomatic meaning) and evaluate on a manually created controlled set (N=147) with matched negatives: out-of-domain random sentences and in-domain literal controls (NLVC), alongside LVC positives. We compare a supe
The proliferation of complex linguistic phenomena in AI models necessitates a deeper understanding of how different learning paradigms perform, particularly for nuanced language processing tasks like multiword expression classification.
Improving AI's ability to accurately classify multiword expressions, especially in languages like Turkish with challenging idiomatic structures, is crucial for advancing natural language understanding and real-world AI applications.
This research contributes to refining methodologies for training AI models on complex linguistic data, potentially leading to more robust and accurate language models capable of disambiguating literal versus idiomatic meanings.
- · AI researchers (NLP)
- · Language model developers
- · Companies building multilingual AI applications
Improved performance of AI systems in understanding nuanced, idiomatic language.
Reduced errors in machine translation and conversational AI for languages with rich idiomatic expressions.
Enhanced AI capability to navigate cultural specificities embedded in language, potentially influencing cross-cultural communication tools.
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Read at arXiv cs.AI