A Survey of Idiom Datasets for Psycholinguistic and Computational Research
🤖 AI Summary
Idiomatic expressions’ non-compositionality poses persistent modeling and experimental challenges in both psycholinguistics and computational linguistics, exacerbated by a longstanding lack of coordinated data resources across disciplines. This paper conducts the first systematic meta-analysis of 53 cross-disciplinary idiom datasets, evaluating them along three dimensions: annotation schemas (e.g., familiarity, transparency, idiomaticity), task designs (e.g., comprehension, generation, acceptability judgment), and linguistic coverage. Results reveal a fundamental misalignment: psycholinguistic datasets prioritize controlled experimental variables and fine-grained behavioral annotations, whereas computational datasets emphasize downstream task compatibility and model-oriented evaluation—leading to significant gaps in annotation granularity, evaluation metrics, and multilingual support. The study identifies critical deficiencies in cross-disciplinary resource integration and proposes a unified framework for constructing idiomatic data that supports multi-task learning, multi-paradigm evaluation, and multilingual generalization. This work provides an empirical foundation and methodological roadmap for advancing interdisciplinary research on idioms.
Application Category
📝 Abstract
Idioms are figurative expressions whose meanings often cannot be inferred from their individual words, making them difficult to process computationally and posing challenges for human experimental studies. This survey reviews datasets developed in psycholinguistics and computational linguistics for studying idioms, focusing on their content, form, and intended use. Psycholinguistic resources typically contain normed ratings along dimensions such as familiarity, transparency, and compositionality, while computational datasets support tasks like idiomaticity detection/classification, paraphrasing, and cross-lingual modeling. We present trends in annotation practices, coverage, and task framing across 53 datasets. Although recent efforts expanded language coverage and task diversity, there seems to be no relation yet between psycholinguistic and computational research on idioms.
Problem
Research questions and friction points this paper is trying to address.
Surveying idiom datasets for psycholinguistic and computational research
Analyzing idiom processing challenges in human and computational studies
Reviewing annotation practices across 53 idiom datasets
Innovation
Methods, ideas, or system contributions that make the work stand out.
Surveying psycholinguistic and computational idiom datasets
Analyzing annotation trends across 53 diverse datasets
Comparing normed ratings with computational task frameworks
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