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. |
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Yusuke Ide, Joshua Tanner, Adam Nohejl, Jacob Hoffman, Justin Vasselli, Hidetaka Kamigaito, Taro Watanabe
| Challenge: | Existing datasets for multiword expressions are inconsistently annotated, limited to a single type of MWE, or limited in size. |
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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. |
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Verbal Multiword Expression Identification: Do We Need a Sledgehammer to Crack a Nut? (2020.coling-main)
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| Challenge: | Multiword expressions (MWEs) are word combinations idiosyncratic with respect to syntax or semantics. |
| Approach: | They propose to use a language-independent system to identify previously seen VMWEs by combining filters to obtain the best averaged F-score over 11 languages and the best score for both seen and unseen VMwes. |
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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 . |
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Pre-tokenization of Multi-word Expressions in Cross-lingual Word Embeddings (2020.emnlp-main)
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| Challenge: | Multi-Word Expressions (MWEs) are common in every language, but they are not translated by cross-lingual word embeddings. |
| Approach: | They propose a method for word translation of Multi-Word Expressions (MWEs) they compile lists of MWEs in each language and tokenize them as single tokens before training word embeddings. |
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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. |
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If you’ve seen some, you’ve seen them all: Identifying variants of multiword expressions (C18-1)
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| Challenge: | Multiword expressions (VMWEs) show idiosyncratic variability, which is challenging for NLP applications. |
| Approach: | They propose to use a model to identify variants of previously seen VMWEs by comparing VMWAs with morpho-syntactic variations. |
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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 . |
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Multiword Expression aware Neural Machine Translation (2020.lrec-1)
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| Challenge: | Multiword Expressions (MWEs) are a pervasive phenomenon in all natural languages and challenge NLP applications because of their unpredictable morpho-syntactic and lexico--semantic behaviour. |
| Approach: | They propose to use linguistic resources to improve MWE translation and MWE generation by up to 5.09 BLEU points on MWE test sets. |
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CDLM: Cross-Document Language Modeling (2021.findings-emnlp)
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| Challenge: | Existing language models (LMs) provide powerful representations for internal text structure, but there are important applications for multi-text tasks. |
| Approach: | They propose a pretraining approach that incorporates two key ideas into the masked language modeling objective. |
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