Morocco

This paper presents an enhanced version of the Romanian corpus previously annotated only for verbal multiword expressions. The new release extends the annotation to multiword expressions of other parts of speech, following version 2.0 of the PARSEME guidelines. The corpus has been expanded, its new part was automatically morpho-syntactically annotated based on the Universal Dependencies framework, followed by extensive semi-automatic annotation of multiword expressions across all morphological categories. The paper also reports quantitative data on the updated corpus and discusses the distribution and characteristics of Romanian multiword expressions. We also highlight language-specific annotation challenges and issues arising from the PARSEME 2.0 guidelines.

EACL 2026

Two Birds with One Stone: Annotating Romanian Multiword Expressions with an
Eye to the PARSEME 2.0 Guidelines Applicability

parseme guidelines 2.0

romanian

multiword expressions

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 explores the behavior of neural machine translation models on two newly introduced datasets containing noun-adjective MWEs with different degrees of semantic ambiguity and compositionality. We compare general-domain machine translation systems with fine-tuned models exposed to small subsets of the target MWEs. By assessing the effects of the learning steps and corpus size, we found that carefully designed fine-tuned may improve MWE handling while mitigating catastrophic forgetting. However, our error analysis reveals that models still struggle in several scenarios, particularly when translating MWEs with idiomatic meanings. Both the datasets and the experiments focus on translation involving Galician, English, and Spanish.

Incorporating Multiword Expressions in Galician Neural Machine Translation:Compositionality, Efficiency, and Performance

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.

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

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.

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Incorporating Multiword Expressions in Galician Neural Machine Translation:Compositionality, Efficiency, and Performance

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