| Challenge: | Contextualised word representation models are used to represent idiomaticity in language. |
| Approach: | They propose probing measures to assess if some of the expected linguistic properties of noun compounds are readily available in some standard and widely used representations. |
| Outcome: | The proposed models show that idiomaticity is not yet accurately represented by contextualised models. |
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Assessing the Representations of Idiomaticity in Vector Models with a Noun Compound Dataset Labeled at Type and Token Levels (2021.acl-long)
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| Challenge: | Existing resources for idiomaticity annotation only include ratings at type level . idioms such as noun compounds have been considered a challenge for NLP . |
| Approach: | They present a dataset with human annotations for 280 noun compounds in English and 180 in Portuguese at both type and token levels. |
| Outcome: | The proposed dataset shows that human annotations are not capturing idiomaticity as human annotation models. |
Can Large Language Models Interpret Noun-Noun Compounds? A Linguistically-Motivated Study on Lexicalized and Novel Compounds (2024.acl-long)
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| Challenge: | Noun-noun compounds represent an important challenge for Natural Language Understanding . correct interpretation of noun-nomin compounds is essential for many applications . |
| Approach: | They test whether Large Language Models can interpret the semantic relation between nouns . they also test whether they can abstract from such knowledge to predict the relation . |
| Outcome: | The proposed models can interpret the semantic relation between nouns and compounds using analogical comparisons. |
Generics are puzzling. Can language models find the missing piece? (2025.coling-main)
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| Challenge: | Generic sentences express generalisations about the world without explicit quantification . human biases in stereotypes can be observed in language models, authors say . |
| Approach: | They analyze generic sentences to determine their quantification and quantify their implicit quantifications using language models. |
| Outcome: | The proposed model shows that generics are more context-sensitive than determiner quantifiers and express weak generalisations. |
Rolling the DICE on Idiomaticity: How LLMs Fail to Grasp Context (2025.acl-long)
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| Challenge: | Existing models fail to resolve idiomaticity when it depends on contextual understanding . idiom frequency influences performance but does not guarantee accurate interpretation. |
| Approach: | They propose a novel contrastive dataset to assess whether large language models can effectively leverage context to disambiguate idiomatic meanings. |
| Outcome: | The proposed model performs better on sentences deemed more likely by the model . collocational frequency and sentence probability influence performance but not accuracy . |
Exploring the Representation of Word Meanings in Context: A Case Study on Homonymy and Synonymy (2021.acl-long)
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| Challenge: | Existing models that represent different senses of words in context are not accurate for polysemous words. |
| Approach: | They propose a multilingual dataset that evaluates the ability of models to accurately represent different lexical-semantic relations such as homonymy and synonymy. |
| Outcome: | The proposed models can disambiguate homonyms in context, but fail to represent words with different senses when occurring in similar sentences. |
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models (2021.findings-emnlp)
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| Challenge: | Existing datasets are limited to providing the degree of idiomaticity of expressions along with the literal and, where applicable, (a single) non-literal interpretation of MWEs. |
| Approach: | They propose to use a dataset to test the effectiveness of a language model in generating representations of sentences containing idioms. |
| Outcome: | The proposed model performs reasonably well on the one-shot and few-shot scenarios, but there is scope for improvement in the zero-shot scenario. |
Putting Words in BERT’s Mouth: Navigating Contextualized Vector Spaces with Pseudowords (2021.emnlp-main)
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| Challenge: | a new technique for exploring contextualized vector space is proposed . masked prediction of a word in a sentence allows controlled exploration of the space . |
| Approach: | They propose a method for exploring regions around individual points in a contextualized vector space . they use a static embedding to induce a "pseudoword" vector and masked prediction of a word . |
| Outcome: | The proposed method investigates the geometry of the contextualized space around individual instances of a word . it uses a static embedding to induce a contextualized "pseudoword" vector . |
Measuring Idiomaticity in Text Embedding Models with epsilon-compositionality (2026.eacl-long)
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| Challenge: | Existing studies on compositionality of text embedding models have limited understanding of the principle . idioms have traditionally been seen as non-compositional . |
| Approach: | They propose to use formal definitions to define compositionality in text embedding models . they find that most models differentiate between idiomatic and non-idiomatic phrases . |
| Outcome: | The proposed model is able to differentiate between idiomatic and non-idiomatic phrases, the authors show . |
Examining Cross-lingual Contextual Embeddings with Orthogonal Structural Probes (2021.emnlp-main)
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| Challenge: | Existing studies on whether multilingual embeddings can be aligned in a shared space across languages are lacking. |
| Approach: | They propose to learn a projection based on monolingual annotated datasets and evaluate syntactic and lexical information encoded in a shared cross-lingual embedding space. |
| Outcome: | The proposed model can be used to learn representations for languages with low resources. |
No Context Needed: Contextual Quandary In Idiomatic Reasoning With Pre-Trained Language Models (2024.naacl-long)
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| Challenge: | idiomatic expressions (IEs) are a non-compositional aspect of a text that makes it difficult for a model to comprehend . general purpose PTLMs are negatively affected by the context, as performance increases with its removal. |
| Approach: | They propose to use idiomatic expressions to infer additional meaning from IEs . they argue that only IE-aware models are suitable for idiom- matic reasoning tasks . |
| Outcome: | The proposed models can reason in the presence of idiomatic expressions, the authors show . they show that general purpose PTLMs are negatively affected by the context . |