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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.

EACL 2026 Main Conference

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

multiword expression

performance

diversity

workshop paper

#### *Message from the General Chair, Aline Villavicencio*
I’m delighted and honoured to welcome you to the 19th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2026), taking place in the beautiful city of Rabat, in Morocco, in March 24-29, 2026. EACL is the flagship European conference of the Association and EACL 2026 proudly continues our field’s tradition of excellence in scholarship, innovation, and inclusivity. I am deeply grateful to the many volunteers whose dedication, generosity, and tireless efforts have made this conference possible.
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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. 

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.

PMI MWE Scorer at PARSEME 2.0 Subtask 1: identifying multi-word expres-
sions using pointwise mutual information and universal dependencies

Multiword expressions (MWEs), particularlyidioms, pose persistent challengesfor vision-language systems due to theirnon-compositional semantics and culturallygrounded meanings. This paper presentsGLIMMER, a three-stage hybrid ranking systemthat evaluates how well images expressthe intended meaning of MWEs across 15 languages.Our approach uses LLM-generatedsemantic glosses as multilingual meaning anchors,combined with dual-path embeddingscoring (textual captions and visual features),and LLM-based semantic verification. Evaluatedon the ADMIRE shared task benchmark,GLIMMER achieves competitive performanceacross diverse languages without relying onparallel training data or language-specific resources.The results show that using glossesto anchor meaning helps match idioms withimages across languages and modalities, andthat combining retrieval with reasoning is morerobust than using embeddings alone.

tiberiucarp at MWE-2026 AdMIRe 2: GLIMMER-Gloss-based Image Multiword
Meaning Expression Ranker

We present IPN, our system for Subtask 1 of the PARSEME 2.0 Shared Task, which targets the identification of MWEs in 17 languages. Overall, IPN outperformed a much larger-parameter baseline model, yet a performance gap to the top-performing systems remains. To better understand these results, we investigate Qwen3-32B's suitability for mono-, cross- and multilingual MWE identification. We also explore whether this model benefits from prepending automatically generated thinking data to the gold label during instruction-tuning. We find that target language data is vital for instruction-tuning. Prepending generated thinking data to a subset of the training data slightly improves performance for two out of three languages, but more detailed evaluation is required.

IPN at MWE-2026 PARSEME 2.0 Subtask 1: MWE Identification via Related
Languages and Harnessing Thinking Mode

This paper describes the system submitted by Semantic Stars Team for Subtask 2 of the PARSEME 2.0 shared task (Paraphrasing Multiword Expressions). Our approach addresses the challenge of paraphrasing sentences containing MWEs such that the MWE is removed while the original meaning and grammatical structure are preserved. The paper describes multiple distinct approaches powered by open-weight Large Language Models (LLMs), each employing a combination of different techniques such as prompting, multi-agent pipelines and classical NLP methods. Four distinct methods are tested on the test data in French, including a fifth one combining the results from the first four. We tested with several different open-weight LLMs including Llama3.1:8b, Qwen3:8b and gpt-oss-120b and were able to achieve significant improvements over the baseline, securing the first place on the shared task leader board.

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