Papers with English noun
Systematicity in GPT-3’s Interpretation of Novel English Noun Compounds (2022.findings-emnlp)
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| Challenge: | e.g., stew skillet, swamp squash) are not fully compositional, but highly predictable based on whether the modifier and head refer to artifacts or natural kinds. |
| Approach: | They propose to compare the interpretations of novel English noun compounds with the large language model GPT-3, which is governed by interpretive principles. |
| Outcome: | The results show that the large language model GPT-3 reasoning only about specific lexical items is consistent with the Levin et al.'s theory. |
A Systematic Search for Compound Semantics in Pretrained BERT Architectures (2023.eacl-main)
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| Challenge: | Existing models for noun compounds have been less successful in predicting compositionality than transformers . authors: suboptimal use of encoded information may be a contributing factor . performance of transformer-based models is poor, authors say . |
| Approach: | They propose to use semantic knowledge derived from pretrained BERT to predict compositionality . they find distinct linguistic roles of heads and modifiers are reflected by differences in BERT representations . |
| Outcome: | The proposed model improves on unsupervised implementations of pretrained BERT . empirical properties such as frequency, productivity, and ambiguity affect performance . |
A Couch Potato is not a Potato on a Couch: Prompting Strategies, Image Generation, and Compositionality Prediction for Noun Compounds (2025.findings-acl)
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| Challenge: | a new method to predict the compositionality of English noun compounds is proposed . |
| Approach: | They propose a visual modality and vision transformers to predict the compositionality of English noun compounds. |
| Outcome: | The proposed method compared with a state-of-the-art text-based approach reveals complementary contributions regarding features and degrees of abstractness in English noun compounds. |
Modeling the Evolution of English Noun Compounds with Feature-Rich Diachronic Compositionality Prediction (2025.acl-long)
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| Challenge: | Empirical research directly addressing these issues is limited to a small number of studies suggesting that compounding is a highly productive process. |
| Approach: | They represent English noun compounds as vectors of time-specific values and implement a set of features to classify them for present-day compositionality and assess the informativeness of the corresponding linguistic patterns. |
| Outcome: | The proposed method captures relevant and complementary information across approaches and shows that low-compositional meanings are reflected by a parallel drop in compositionality and sustained semantic change. |
Emergent morpho-phonological representations in self-supervised speech models (2025.emnlp-main)
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| Challenge: | a recent study shows that self-supervised speech models do not represent phonological and morphological phenomena in frequent English noun and verb inflections. |
| Approach: | They study how S3Ms represent phonological and morphological phenomena in English . they propose alternative representational strategies that may support human spoken word recognition . |
| Outcome: | a new study shows that S3M models can represent phonological and morphological phenomena in English . the models can be trained to recognize spoken words in naturalistic, noisy environments . |