Morocco

Multiword Expressions (MWEs) are pervasive in scientific writing, and in specialized domains they include both multiword terminology (e.g., noun compounds) and recurrent academic phrasing. This study profiles MWEs in a large corpus of bioinformatics research articles segmented by IMRaD sections. Building on recent multi-method approaches to scientific MWE identification, we extract MWEs using complementary automated strategies (semantic matching, dependency parsing, controlled vocabularies, and academic formula lists) and compare the resulting inventories by size, form, and IMRaD section distribution. We further quantify cross-document dispersion using document frequency and Gries’ DP to distinguish widely reused expressions from items concentrated in a small subset of articles. Results show that bioinformatics MWEs are predominantly short and nominal, but that extraction methods differ in the extent to which they recover discourse and reporting phraseology. Dispersion is strongly long-tailed across sections with most MWEs being document-specific, while a smaller recurrent core aligns with section function and is enriched for conventional templates and standardized multiword terms. Overall, the findings argue for combining complementary identification methods with dispersion profiling to characterize domain &quot;multiwordness&quot; in a principled and section-sensitive way.

EACL 2026

Beyond Single Words: MWE Identification in Bioinformatics Research Articles
and Dispersion Profiling Across IMRaD

linguistic profiling

imrad structure

dispersion analysis

formulaic language

scientific discourse

domain-specific language

phraseology

mwe identification

multiword expressions

bioinformatics

corpus linguistics

### Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

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ACL 2025

Welcome to The 63rd Annual Meeting of the Association for Computational Linguistics (ACL 2025)

Multiword Expressions (MWEs) are pervasive in scientific writing, and in specialized domains they include both multiword terminology (e.g., noun compounds) and recurrent academic phrasing. This study profiles MWEs in a large corpus of bioinformatics research articles segmented by IMRaD sections. Building on recent multi-method approaches to scientific MWE identification, we extract MWEs using complementary automated strategies (semantic matching, dependency parsing, controlled vocabularies, and academic formula lists) and compare the resulting inventories by size, form, and IMRaD section distribution. We further quantify cross-document dispersion using document frequency and Gries’ DP to distinguish widely reused expressions from items concentrated in a small subset of articles. Results show that bioinformatics MWEs are predominantly short and nominal, but that extraction methods differ in the extent to which they recover discourse and reporting phraseology. Dispersion is strongly long-tailed across sections with most MWEs being document-specific, while a smaller recurrent core aligns with section function and is enriched for conventional templates and standardized multiword terms. Overall, the findings argue for combining complementary identification methods with dispersion profiling to characterize domain "multiwordness" in a principled and section-sensitive way.

workshop paper

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Welcome to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL). EACL 2026 will be held in Rabat, Morocco, from March 24–29, 2026. 

Multiword expressions are an important area of study in linguistics and natural language processing as they represent combination of words that function as a single unit, and display properties that cannot be predicated fully from their individual components. This paper describes annotated corpora of about 3000 multiword expressions across syntactic categories in Marathi. This is the first exhaustive resource for Marathi which includes both verbal and non-verbal multiwords. In order to develop the guidelines for annotation, we have used the existing literature on the identification and classification of these expressions. Following the PARSEME 2.0 guidelines, we discuss the categories of multiwords and their behaviour in the corpus. Throughout the annotation process, we encounter variability in compositionality and syntactic realization and discuss our design decisions during annotation. Such a dataset will further our understanding of how grammatical structure can be integrated with lexically stored multiword units in Marathi.

The Lock, Stock, and Barrel of Marathi Multiwords

Despite recent significant advances, idioms, like other forms of figurative language, present a challenge to natural language processing (NLP). Benchmark corpora are essential for improving the current models on understanding idioms. However, such corpora are only available for a limited set of languages. In this paper, we introduce our ongoing work on a benchmark corpus of Turkish idioms. Our corpus is structured for testing both idiom recognition and idiom understanding. The corpus is currently consists of 200 instances with sentences including idiomatic use, their literal paraphrases, similar sentences with no entailment, and non-idiomatic use of the idiomatic expressions when possible. We describe the methodology used to create the corpus, as well as initial experiments with a selection of LLMs.

A Benchmark Corpus for Turkish Idiomatic Expression Understanding

Multiword expressions (MWEs) are good examples of a phenomenon where identification systems struggle with generalisation: MWE present in the test set but absent in the training set are rarely identified. This raises the question of the diversity of the test set, relative to that of the train set, and how this impacts performance. We set out to measure how much diversity of a train corpus increases when adding individual MWEs from the test corpus, and how this increase impacts MWE identification performance. We measure diversity across a three-dimension framework and find mostly consistent negative correlations with performance in 14 languages and 8 systems.

Diversity patterns run deep: Impact of diversity intake on multiword expression
identification

This paper describes the system submitted for the $\textbf{MWE 2026 Shared Task}$ (AdMIRe 2.0 Subtask A). The submission focused on a text-centric approach, reframing the idiom-image alignment task as a sentence-pair classification problem using $\textbf{mBERT}$ (Multilingual BERT). The submitted system relied on full fine-tuning using only the English training data, achieving a Top-1 Accuracy of approximately $\textbf{0.30}$ on the blind test set. Following the evaluation phase, significant limitations were identified in the cross-lingual generalization of the base model. In a post-evaluation study, the backbone was upgraded to $\textbf{XLM-RoBERTa-Large-XNLI}$, incorporating $\textbf{Low-Rank Adaptation (LoRA)}$ and utilizing the full multilingual dataset with hard negative mining. These improvements boosted the accuracy to $\textbf{0.41}$, demonstrating the necessity of NLI-specific pre-training and parameter-efficient tuning for MWE-aware multimodal tasks.

IdiomRanker-X at MWE-2026 AdMIRe 2: Multilingual Idiom-Image Alignment
via Low-Rank Adaptation of Cross-Encoders

This paper presents the system developedby team alexandru412 for the AdMIRe 2.0Shared Task. We participated in the Text-Onlytrack, ranking images based on idiomatic us-age without accessing pixel data. Our approachcombines a strict list-wise ranking strategy withsystematic test-time augmentation. We fine-tuned a Large Language Model (LLM) on En-glish and Portuguese data and relied on zero-shot transfer for other languages. Our systemachieved the 3rd place in the Text-Only track.

alexandru412 at MWE-2026 AdMIRe 2.0: Advancing Multimodal Idiomaticity
Representation

This paper describes a multilingual system for automatic multiword expression identification for PARSEME 2.0 Subtask 1. We formulate MWE identification as a token-level sequence labeling problem using a BIO tagging scheme and fine-tune XLM-RoBERTa-base on PARSEME 2.0. We mainly investigate cross-lingual interactions on language pairs, and test hypotheses whether using a given language pair for training improves MWE detection performance on both or one of the languages. Then, we apply selected successful language pairs on PARSEME 2.0 MWE Identification task. Experiments are conducted independently for a subset of the languages given in PARSEME 2.0, for a total of 8 languages. Our approach achieves strong token-based and span-based F1 scores across diverse languages, and we observe that training with even distant language pairs may result in improvement on at least one of the languages. We publish our code at https://github.com/ahmeterdem1/parseme-blg505

Cross Lingual BERT at PARSEME 2.0 Subtask 1: Can Cross-Lingual Interac-
tions Improve MWE Identification?

We address AdMIRe 2.0, a static image ranking task where a sentence containing a potentially idiomatic expression is paired with five image--caption candidates, and the goal is to rank the candidates by semantic compatibility with the intended idiomatic or literal meaning. We propose IMMCAN, which keeps XLM-R and Jina-CLIP-v2 frozen and learns a lightweight two-stage cross-attention fusion, caption--image grounding followed by idiom-to-multimodal conditioning, to predict a compatibility score per candidate. We also evaluate caption-only augmentation via back-translation and synonym substitution, and compare regression and rank-class formulations. On AdMIRe 1.0, text-only achieves higher test top-image accuracy than VLM-grounded modeling. In contrast, on AdMIRe 2.0 zero-shot, adding visual patch grounding improves both accuracy and NDCG indicating better cross-lingual ranking transfer.

IMMCAN: Idiom Multimodal Cross-Attention Network

Multi-Word Expressions (MWEs) pose a significant challenge for natural language processing systems due to their idiosyncratic semantic and syntactic properties. This paper describes our system for the PARSEME 2.0 Shared Task on automatic identification of verbal MWEs across 17 typologically diverse languages. Our approach combines multilingual BERT with explicit Part-of-Speech (POS) feature injection through a dual-head architecture that jointly performs BIO-based identification and category classification. We further investigate extensions, including Conditional Random Field (CRF) decoding for structured prediction, focal loss for addressing class imbalance, and model ensembling for improving discontinuous MWE detection. Our official submission achieves a global MWE-based F1 score of 48.39%, securing second place in the shared task. Ablation studies reveal a strong synergy between POS features and CRF decoding, with the combined approach yielding the best single-model performance. Furthermore, ensembling models trained with different objectives improves both overall F1 score and discontinuous MWE scores, demonstrating the importance of training diversity for capturing non-adjacent syntactic patterns.

Sahara Tokenizers at PARSEME 2.0 Subtask 1: Combining Contextual Embed-
dings with Structural Decoding for Multi-Word Expression Detection

Idiomatic expressions pose a fundamental challenge for multimodal understanding due to their non-compositional semantics, while pretrained vision–language models tend to over-rely on literal visual alignments. We address this issue in the context of the AdMIRe 2.0 multimodal idiomatic image ranking task by introducing CARIM (Category-Aware Reasoning for Idiomatic Multimodality), an inference-time framework that injects structured semantic reasoning without end-to-end retraining.Experiments on the official Codabench leaderboard demonstrate that CARIM achieves competitive Top-1 Accuracy and nDCG across multiple languages. Additional post-competition evaluation on the released test annotations further shows that CARIM maintains robust multilingual performance, highlighting the effectiveness of inference-time category-aware reasoning for multimodal idiomatic grounding.

CARIM-- Category-Aware Reasoning for Idiomatic Multimodality

Multi-word expressions (MWEs) remain a challenge for NLP systems due to their syntactic variability and non-compositional semantics, that is why this issue was proposed as shared task within Unidive organization. With increasing popularity of large language models (LLM) it is important to continue researching alternative solutions. One of classical approaches for identifying MWEs is calculating pointwise mutual information (PMI), but this is a purely statistical approach that cannot unveil the links between words in natural text. To fix this issue we propose this paper with a simple syntax-aware PMI method that leverages Universal Dependency (UD) trees (Nivre et al.,2016) to model co-occurrence between syntactically related words. By computing PMI over dependency-linked word pairs and aggregating these scores, we aim to improve surface-based methods. Opposed to expectations, our experiment shows that classical statistical approach gets better results in identifying MWEs partially. Still, this approach is aimed to find a balance between lightweight calculations as opposed to LLMs and precision in results.

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The Lock, Stock, and Barrel of Marathi Multiwords

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