Morocco

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.

EACL 2026 Main Conference

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

pointwise mutual information

multi word expressions

universal dependencies

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.
For the first time EACL is being hosted in the African continent. This is an important milestone for our community, and we are grateful to our Moroccan hosts for enabling this historic moment by bringing this edition of EACL to Rabat. We are also delighted that the Second Arabic NLP School is co-located with EACL. We hope attendees enjoy this wonderful opportunity to strengthen ties with the Computational Linguistics communities across the African continent. *[Read full message](https://drive.google.com/file/d/14NlmHvwM6fPJuMmOvVh7K0vtQbEyv3SZ/view?usp=sharing)*<br><br>

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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 (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.

Semantic Stars at MWE-2026 PARSEME 2.0 Subtask 2: Alternative Approaches
for MWE Paraphrasing

This paper describes MorphoFiltered-Gemini, a multilingual system submitted to the PARSEME 2.0 shared task on multiword expression (MWE) identification. The system relies on Google Gemini 2.0 Flash-Lite to generate MWE predictions using zero-shot and selectively applied few-shot prompting, without fine-tuning or language-specific resources. To reduce the tendency of large language models to over-generate MWEs, we introduce a lightweight morphological post-filter that removes unlikely constructions while preserving high-precision patterns.Rather than optimizing peak performance for individual languages, our approach prioritizes precision and cross-lingual robustness. As a result, the system exhibits stable behavior across 17 typologically diverse languages and achieves the highest Shannon evenness score among all submitted systems. The experimental results highlight a clear trade-off between recall-oriented LLM prompting strategies and precision-oriented filtering, and show that simple linguistic constraints can effectively improve the stability of LLM-based multilingual MWE identification systems.

MorphoFiltered-Gemini at MWE-2026 PARSEME 2.0 Subtask 1: Tackling LLM
Overgeneration via Universal POS-based Constraints

This paper presents our methods for the AdMIRe 2.0 shared task, which addresses multilingual and multimodal idiom understanding. Our submission focuses on the text-only track. Specifically, we employ an ensemble of three large language models (LLMs) to directly perform the presented image ranking task. Each model independently produces a ranking of the candidate images, and we aggregate their outputs using a hard voting strategy to determine the final prediction. This ensemble learning framework leverages the complementary strengths of different LLMs, improving robustness and reducing the variance of individual model predictions.

LST at MWE-2026 AdMIRe 2: Advancing Multimodal Idiomaticity Representa-
tion

This paper describes MISP (Multilingual Id-iomatic Sentence Paraphrasing), a system sub-mitted to the PARSEME 2.0 MultilingualShared Task on Identification and Paraphras-ing of Multiword Expressions (MWEs). Weparticipated in Subtask 2 on MWE para-phrasing and developed our system based onQwen3-4B-Instruct fine-tuned on syntheticPortuguese MWE paraphrases. We appliedMISP not only to Portuguese, but also to Frenchand Romanian, aiming to leverage cross-lingualtransfer within related languages, with ours be-ing the only submission for Portuguese. Ourresults indicate that MISP struggles to generateparaphrases that both rephrase and preserve theoriginal meaning of the MWE. Additionally,instruction fine-tuning does not appear to im-prove performance. Overall, our findings high-light the challenges of paraphrasing MWEs,particularly in a cross-lingual setting

UniBO at MWE-2026 PARSEME 2.0 Subtask 2: A Cross-lingual Approach to
Multiword Expression Paraphrasing

This paper presents our system for the MWE-2026 ADMiRe 2.0 shared task, which aimedto advance multimodal idiomatic understand-ing across 15 languages. We address the taskof selecting, from a set of five images, theone that best represents either the literal oridiomatic meaning of a given compound incontext. Our approach follows a multi-steppipeline: a large language model (LLM) firstdetermines whether the compound is used lit-erally or idiomatically and generates auxiliarytext, consisting of an idiomatic meaning expla-nation and a visual description of the literalmeaning. An ensemble of three CLIP modelsthen identifies the two images most semanti-cally similar to the appropriate generated textvia a voting mechanism. Finally, the LLM se-lects the best image from these two candidates.

DCSN-NLP at MWE-2026 AdMIRe 2: Bridging Literal and Figurative Meaning
Through Hierarchical Multimodal Reasoning

This paper presents our system for AdMIRe 2 (Advancing Multimodal Idiomaticity Representation), a shared task on multilingual multimodal idiom understanding. The task focuses on ranking images according to how well they depict the literal or idiomatic usage of potentially idiomatic expressions (PIEs) in context, across 15 languages and two tracks: a text-only track, and a multimodal track that uses both images and captions. To tackle both tracks, we propose a hybrid zero-shot pipeline built on large vision–language models (LVLMs). Our system employs a chain-of-thought prompting scheme that first classifies each PIE usage as literal or idiomatic and then ranks candidate images by their alignment with the inferred meaning.A primary–fallback routing mechanism increases robustness to safety-filter refusals, while lightweight post-processing recovers consistent rankings from imperfect model outputs.Without any task-specific fine-tuning, our approach achieves 55.9\% Top-1 Accuracy in the text-only track and 60.1\% in the multimodal (text+image) track, ranking first overall on the official leaderboard. These results suggest that carefully designed zero-shot LVLM pipelines can provide strong baselines for multilingual multimodal idiomaticity benchmarks.

ITUNLP at MWE-2026 AdMIRe 2: A Zero-Shot LLM Pipeline for Multimodal
Idiom Understanding and Ranking

This paper presents our approach to the PARSEME 2.0 Shared Task on Romanian, covering both Identification (Subtask 1) and Paraphrasing (Subtask 2). While Large Language Models (LLMs) excel at semantic generation, we hypothesize that they lack the structural precision required for MWE identification, leading to "boundary hallucinations" that compromise downstream simplification. Our Rank 1 results on Romanian confirm this: a specialized encoder (RoBERT) using standard sequence labeling outperforms both few-shot LLMs and complex structural parsers (MTLB-STRUCT). This justifies our proposed pipeline: using encoders as precise ``pointers'' to guide the generative power of LLMs.

Archaeology at WE-2026 PARSEME 2.0 Subtask 1 and 2: Parsing is for Encoders, Paraphrasing is for LLMs

We describe a zero-shot system for AdMIRe 2.0, a shared task on multimodal understanding of potentially idiomatic expressions (PIEs). Given a context sentence with a PIE and five candidate images, the system predicts whether the usage is literal or idiomatic and ranks images by how well they match the intended meaning. We use closed-source large multimodal models and compare prompting pipelines from direct one-step ranking to modular multi-step pipelines that separate sense prediction, PIE-focused image semantics, and final ranking. All steps produce constrained JSON outputs to enable deterministic parsing and composition. In the official AdMIRe 2.0 evaluation on CodaBench, our best pipeline achieves an average Top-1 accuracy of 0.52 and an average nDCG score of 0.70 across the 12 languages we submitted. We obtain the best score among submitted systems in 10 of these languages.

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