No more beating about the bush : A Step towards Idiom Handling for Indian Language NLP (L18-1)
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| Challenge: | idioms are a part of natural language and are difficult to learn with a parallel corpora database. |
| Approach: | They propose to use a parallel idiom dataset to train two NLP subtasks . they show significant improvement in the two subtask training without the idiomatic dataset . |
| Outcome: | The proposed model improves on baseline models with the idiom dataset for two NLP applications. |
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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. |
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| Challenge: | Neural Machine Translation (NMT) has been widely used in recent years with significant improvements for many language pairs. |
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Dedicated Language Resources for Interdisciplinary Research on Multiword Expressions: Best Thing since Sliced Bread (2020.lrec-1)
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| Challenge: | Multiword expressions are challenging for disciplines like NLP, psycholinguistics and second language acquisition due to their more or less fixed character. |
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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). |
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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. |
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ID10M: Idiom Identification in 10 Languages (2022.findings-naacl)
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| Challenge: | Identifying and understanding idioms in context is a key goal and challenge in Natural Language Understanding tasks. |
| Approach: | They propose a multilingual Transformer-based system for the identification of idioms and a manually-curated evaluation benchmark. |
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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. |
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BhashaSutra: A Task-Centric Unified Survey of Indian NLP Datasets, Corpora, and Resources (2026.acl-long)
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| Challenge: | Existing reviews focus on a few high-resource languages or embed Indian languages within broad multilingual settings, limiting coverage of low-resourced and culturally diverse varieties. |
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It’s Not a Walk in the Park! Challenges of Idiom Translation in Speech-to-text Systems (2025.acl-long)
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| Challenge: | idioms are defined as words with a figurative meaning not deducible from their individual components. |
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Automatic Evaluation and Analysis of Idioms in Neural Machine Translation (2023.eacl-main)
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| Challenge: | Neural machine translation (NMT) struggles with the translation of rare multi-word expressions (MWEs). |
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