Getting BART to Ride the Idiomatic Train: Learning to Represent Idiomatic Expressions (2022.tacl-1)
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| Challenge: | Prior work has identified deficiencies in their contextualized representation stemming from the underlying compositional paradigm of representation. |
| Approach: | They propose to use an adapter as a lightweight non-compositional language expert trained on idiomatic sentences to build idiomity into BART. |
| Outcome: | The proposed approach improves idiomaticity over baselines and up to 25% higher sequence accuracy on idiom processing tasks. |
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Idiomatic Expression Identification using Semantic Compatibility (2021.tacl-1)
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| Challenge: | Existing approaches to localize idiomatic expressions have limited views of their generalizability to new idioms. |
| Approach: | They propose a multi-stage neural architecture to detect whether a sentence has an idiomatic expression and localize it when it occurs in a figurative sense. |
| Outcome: | The proposed model achieves state-of-the-art on three of the largest datasets with idiomatic expressions of varied syntactic patterns and degrees of non-compositionality. |
Crossing the Threshold: Idiomatic Machine Translation through Retrieval Augmentation and Loss Weighting (2023.emnlp-main)
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| Challenge: | idioms are common in everyday language, but often pose a challenge to translators because their meanings do not follow from the meanings of their parts. |
| Approach: | They propose to use retrieval-augmented models to increase the accuracy of a strong pretrained machine translation model on idiomatic sentences by up to 13%. |
| Outcome: | The proposed techniques improve the accuracy of a strong pretrained model on idiomatic sentences by up to 13% in absolute accuracy, and holds potential benefits for non-idiomatic phrases. |
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. |
Unified Representation for Non-compositional and Compositional Expressions (2023.findings-emnlp)
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| Challenge: | Existing representations of non-compositional language are based on BART, but they are not as accurate as the state-of-the-art IE representation model, GIEA. |
| Approach: | They propose a language model, PIER+, that builds on BART and can generate semantically meaningful and contextually appropriate representations for English potentially idiomatic expressions. |
| Outcome: | The proposed model achieves 33% higher homogeneity score on embedding clustering than BART, while sacrificing performance on NLU tasks (+/- 1% accuracy) |
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 . |
IEKG: A Commonsense Knowledge Graph for Idiomatic Expressions (2023.emnlp-main)
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| Challenge: | Prior work on IE comprehension has focused on detecting idiomaticity, but this fails to account for IEs' non-compositionality. |
| Approach: | They construct a commonsense knowledge graph for figurative interpretations of IEs that can be used to convert PTLMs into knowledge models that encode and infer commonsensical knowledge related to IE use. |
| Outcome: | The proposed model can generalize to IEs unseen during training. |
CLIX: Cross-Lingual Explanations of Idiomatic Expressions (2025.findings-acl)
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| Challenge: | Existing definition generation systems are difficult to use in second language learning due to the presence of unfamiliar words and grammar. |
| Approach: | They propose to use cross-lingual explanations of idiomatic expressions to support vocabulary expansion for language learners. |
| Outcome: | The proposed system is able to explain idiomatic expressions in non-standard language. |
Can Transformer be Too Compositional? Analysing Idiom Processing in Neural Machine Translation (2022.acl-long)
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| Challenge: | Unlike literal expressions, idioms’ meanings do not follow from their parts, posing a challenge for neural machine translation (NMT). |
| Approach: | They examine the mechanics of the dominant NMT model, Transformer, and their effect on their understanding of idioms. |
| Outcome: | The proposed model over-generates compositional, literal translations and is unable to translate idioms accurately. |
Beyond Multiword Expressions: Processing Idioms and Metaphors (P18-5)
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| Challenge: | idioms and metaphors processing is a rapidly growing area in NLP, says dr. s. robertson . idiomatic idiomas are characteristic to all areas of human activity and to all types of discourse. |
| Approach: | This tutorial will provide attendees with a clear notion of idioms and metaphors . it will provide them with computational models of linguistic characteristics and methods . |
| Outcome: | This tutorial aims to provide attendees with a clear notion of the linguistic characteristics of idioms and metaphors . it outlines how to model idiomatic idiomes and their processing and what resources are available to support their use . |
Potential Idiomatic Expression (PIE)-English: Corpus for Classes of Idioms (2022.lrec-1)
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Tosin Adewumi, Roshanak Vadoodi, Aparajita Tripathy, Konstantina Nikolaido, Foteini Liwicki, Marcus Liwicki
| Challenge: | Potential Idiomatic Expression (PIE) dataset for NLP in English contains over 20,100 samples with almost 1,200 cases of idioms from 10 classes (or senses). |
| Approach: | They present a large Potential Idiomatic Expression (PIE) dataset for Natural Language Processing (NLP) in English. |
| Outcome: | The proposed dataset contains over 20,100 samples with almost 1,200 cases of idioms (with their meanings) from 10 classes (or senses). |