Morocco

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.

EACL 2026

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

multiagent ai

agentic

parseme

#### *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. 

workshop paper

We introduce Semantic Echo Pathways (SEP), a new approach for modeling the cross-domain evolution of medical language. Using continual neural topic models (CoNTM) trained separately on scientific literature, clinical notes, and public health-related data, we track linguistic drift and identify points where concepts change meaning. We propose three novel metrics: Cross-Domain Drift Score, Temporal Echo Lag, and Semantic Mutation Patterns to quantify how medical language travels between the scientific, clinical, and public domain. Applications to evolving concepts such as "long COVID", diagnostic category changes reveal previously undocumented patterns of medical-semantic evolution. Our results bridge computational modeling with the human-centered perspectives of medical humanities, offering clear, domain-aware maps of how medical language shifts across time and domains, and combining quantitative analysis with linguistic and clinical insight.

Semantic Echo Pathways (SEP): Tracing How Medical Language Propagates and Transforms

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.

ITUNLP2 at MWE-2026 AdMIRe 2: Modular Zero-Shot Pipelines for Multi-
modal Idiom Grounding and Ranking

Multiword expressions (MWEs) have been a major challenge in NLP for decades and research on MWEs was driven notably by shared tasks, including those organized by the PARSEME community. We report the organisation and the results of edition 2.0 of the PARSEME shared task. For the first time, all syntactic categories are covered: verbal, nominal, adjectival, adverbial and functional. We rely on edition 2.0 of the PARSEME corpus, annotated for all these categories in 17 languages. We create a new dataset with paraphrases of sentences containing idioms in 14 languages, and defining a new subtask dedicated to MWE paraphrasing. We extend our evaluation protocol by measuring both performance and diversity of systems, and including manual evaluation in paraphrasing. 10 systems, including the baseline, participated in the MWE identification subtask and 5 in the paraphrasing subtask. Results are promising, but known MWE identification challenges remain unsolved. Performance correlates positively with diversity in MWE identification, and negatively in MWE paraphrasing.

Edition 2.0 of the PARSEME shared task on multilingual identification and para-
phrasing of multiword expressions

Idiomatic expressions present a unique chal-lenge in NLP, as their meanings are often notdirectly inferable from their constituent words.Despite recent advancements in large languagemodels, idiomaticity remains a significant ob-stacle to robust semantic representation. Wepresent datasets and task results for MWE-2026 Shared Task 2: Advancing MultimodalIdiomaticity Representation 2 (AdMIRe 2),which challenges the community to assess andimprove models’ ability to interpret idiomaticexpressions in multimodal contexts across mul-tiple languages. Participants competed in animage ranking task in which, for each item,systems receive a context sentence containinga potentially idiomatic expression (PIE) andfive candidate images. Participating systemsare required to predict the sentence type (i.e.,idiomatic vs. literal) for the given context andrank the images by how well they depict the in-tended meaning in that context. Among the par-ticipating systems the most effective methodsinclude pipelines utilizing closed-source com-mercial models such as Gemini 2.5 and GPT-5, and employing chain-of-thought reasoningstrategies. Methods to mitigate language mod-els’ bias towards literal interpretations and en-sembles to smooth out variance were common.

Next from EACL 2026

Semantic Echo Pathways (SEP): Tracing How Medical Language Propagates and Transforms

Similar lecture

Edition 2.0 of the PARSEME shared task on multilingual identification and para- phrasing of multiword expressions