Challenge: Existing datasets for multiword expressions are inconsistently annotated, limited to a single type of MWE, or limited in size.
Approach: They propose to use a new interface to generate MWE annotations for the first time in a dataset of MWE identification.
Outcome: The proposed model outperforms existing models on the DiMSUM dataset.

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MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora (2020.lrec-1)

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Challenge: Existing bilingual or multi-lingual MWE corpora are limited for multilingual use . only 871 pairs of English-German MWEs are available for research .
Approach: They present a collection of bilingual and multi-lingual MWEs extracted from parallel corpora.
Outcome: The available bilingual or multi-lingual MWE corpus is very limited . the collection is a small collection of 871 pairs of English-German MWEs .
Detecting Multiword Expression Type Helps Lexical Complexity Assessment (2020.lrec-1)

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Challenge: Multiword expressions (MWEs) represent lexemes that should be treated as single lexical units due to their idiosyncratic nature.
Approach: They re-annotate a complex word identification shared task 2018 dataset . they find that a lexical complexity assessment system benefits from the information .
Outcome: The proposed dataset provides valuable information for the text simplification community.
Construction of Large-scale English Verbal Multiword Expression Annotated Corpus (L18-1)

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Challenge: In this paper, we focus on verbal MWEs, whose accurate recognition is challenging because they could be discontinuous.
Approach: They conduct large-scale annotations of VMWEs on the Wall Street Journal portion of Ontonotes . they first construct a VMwe dictionary based on the english-language Wiktionary .
Outcome: The proposed resource annotates 7,833 VMWE instances belonging to various categories . the authors hope the results will help to develop models for MWE recognition and dependency parsing .
Binary Token-Level Classification with DeBERTa for All-Type MWE Identification: A Lightweight Approach with Linguistic Enhancement (2026.findings-eacl)

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Challenge: Current approaches focus on specific MWE types, such as transformer-based models that incorporate linguistic features like dependency parsing for verbal discontinuous patterns.
Approach: They propose a binary token-level classification approach that integrates linguistic feature integration and data augmentation to improve multiword expression (MWE) identification.
Outcome: The proposed model outperforms the Qwen-72B model on the CoAM dataset by 12 points while using 165 times fewer parameters.
Unsupervised Paraphrasing of Multiword Expressions (2023.findings-acl)

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Challenge: Existing methods for paraphrasing multiword expressions in context are unsupervised . multiwords are notoriously difficult to model because the meaning of the whole can diverge substantially from that of the component words.
Approach: They propose an unsupervised approach to paraphrasing multiword expressions in context using monolingual corpus data and pre-trained language models.
Outcome: The proposed method outperforms all unsupervised systems and rivals supervised systems on the SemEval 2022 idiomatic text similarity task.
Benchmarking the Performance of Machine Translation Evaluation Metrics with Chinese Multiword Expressions (2024.lrec-main)

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Challenge: Multiword Expressions (MWEs) are hard nuts for many natural language processing tasks.
Approach: They annotate 28 types of Chinese MWEs and then examine 31 MTE metrics on groups of sentences containing different MWE.
Outcome: The results show that MT systems and MTE metrics still suffer from MWEs .
Easy as PIE? Identifying Multi-Word Expressions with LLMs (2025.emnlp-main)

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Challenge: Multiword expressions (MWEs) are a semantically non-compositional subclass of multiword expression . authors show that prompt-based LLMs can perform competitively with supervised models .
Approach: They propose a prompt-based approach to identify idiomatic expressions in running text . they find prompt-driven LLMs can perform competitively with supervised models .
Outcome: The proposed approach can perform well with supervised models on annotated data.
Cross-type French Multiword Expression Identification with Pre-trained Masked Language Models (2024.lrec-main)

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Challenge: Multiword expressions (MWEs) have linguistic features that distinguish them from regular word groupings.
Approach: They propose a combination of two systems that learn verbal multiword expressions and non-verbal MWEs to improve performance on a cross-type dataset .
Outcome: The proposed system improves the F1 score on a french treebank with VMWEs and nVMWES training data.
For a Fistful of Puns: Evaluating a Puns in Multiword Expressions Identification Algorithm Without Dedicated Dataset (2025.findings-emnlp)

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Challenge: a recent study has shown that multiword expressions and wordplays impact their performance and are idiosyncratic and pervasive across languages.
Approach: They propose an alignment-based PMWE identification and tagging algorithm to identify different types of PMWEs.
Outcome: The proposed algorithm can identify different types of PMWEs and perform a snowclone detection task in English.
A Large Automatically-Acquired All-Words List of Multiword Expressions Scored for Compositionality (L18-1)

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Challenge: Existing literature on semantically idiosyncratic multiword expressions is limited to English . idiomatic expressions are phraseological units consisting of more than one lexeme and exhibit some kind of idiom.
Approach: They propose to make available a large automatically-acquired all-words list of English multiword expressions scored for compositionality.
Outcome: The proposed list improves the BLEU scores of the English multiword expressions.

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