Morocco

This study investigates whether eye-tracking measures predict if a word is the final token of a multi-word expression (MWE), focusing on two understudied MWE types: fixed expressions (e.g., \textit{due to}) and phrasal verbs (e.g., \textit{turn out}). Using mixed-effects logistic regression, we compared tokens in MWE contexts with the same tokens in non-MWE contexts. Results reveal a clear difference in processing. For fixed expressions, reading-time measures significantly predict MWEhood. In contrast, phrasal verbs show no consistent predictive effects. Additionally, we compared the reading-time models to models that included GPT-2 surprisal as a predictor. While surprisal does predict MWEhood, it fails to capture the distinction between types. These findings highlight the need to consider MWE typology in models of formulaic language processing.

EACL 2026

Cognitive Signatures of Multi-Word Expressions: Reading-Time and Surprisal

eye-tracking measures

cognitive processing

multi-word expressions

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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. 

In recent years, language models, both encoder-only and generative, have been applied to a variety of downstream NLP tasks, includingsequence labeling tasks like automatic multi-word expression identification (MWEI). Multiple studies show that, in general, fine-tunedencoder-only models like BERT tend to outperform pretrained generative LLMs on downstream tasks (Arzideh et al., 2025; Ochoa et al.,2025; Bucher and Martini, 2024; Sebok et al., 2025). However, such comparisons are sparse for MWEI, in particular for French, in partdue to the lack of comprehensive gold-standard datasets. In this study, we address this research gap by comparing CamemBERT with gpt-oss and Qwen3 for MWEI, using the French subcorpus of the newly released PARSEME dataset. CamemBERT outperforms both LLMs by large margins in precision, recall, and F1. We complement this numerical evaluation with a qualitative analysis of prediction errors.

Cheese it up: CamemBERT Outperforms Large Language Models for Identifica-
tion of French Multi-word Expressions

IT-systems generate log messages containing important information about the system's health. To gather information about system entities, we extract technical terms and proper nouns as multi-word expressions (MWEs) from a wide range of log messages from 16 different real systems. We apply Gries' information-theoretic approach which iteratively calculates the best MWE candidates using an eight-dimensional ranking method. These candidates are evaluated in an annotation study, achieving a precision of 66 %. This value is significantly higher than evaluations on general-purpose texts, demonstrating the higher occurrence of compound technical terms and proper nouns in log messages. The MWEs found can be used to reduce the number of nodes in a system behavior graph while increasing the information density of the nodes.

Extracting Multi-Word Expressions Representing Technical Terms and Proper
Nouns in Log Messages

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.

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

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.

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