etiennes

• Wold, Sondre; Simon, Étienne; Velldal, Erik & Øvrelid, Lilja (2026). Measuring Idiomaticity in Text Embedding Models with epsilon-compositionality. In Demberg, Vera; Inui, Kentaro & Marquez, Lluís (Ed.), Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). Association for Computational Linguistics (ACL). ISSN 9798891763807. p. 2239–2252. doi: https:/aclanthology.org/2026.eacl-long.99/. Full text in Research Archive Show summary

  The principle of compositionality, which concerns the construction of meaning from constituent parts, is a longstanding topic in various disciplines, most commonly associated with formal semantics. In NLP, recent studies have focused on the compositional properties of text embedding models, particularly regarding their sensitivity to idiomatic expression, as idioms have traditionally been seen as non-compositional. In this paper, we argue that it is unclear how previous work relates to formal definitions of the principle. To address this limitation, we take a theoretically motivated approach based on definitions in formal semantics. We present 𝜀-compositionality, a continuous relaxation of compositionality derived from these definitions. We measure 𝜀-compositionality on a dataset containing both idiomatic and non-idiomatic sentences, providing a theoretically motivated assessment of sensitivity to idiomaticity. Our findings indicate that most text embedding models differentiate between idiomatic and non-idiomatic phrases, although to varying degrees.

• Simon, Étienne; Olsen, Helene Bøsei; Villar, Ramón Carreño; Mishra, Rahul; Arefyev, Nikolay & Yilmaz, Mert Can [Show all 8 contributors for this article] (2025). Abstractive Event Analysis of Armed Conflicts: Introducing the UCDP-AEC Dataset. In Wartena, Christian & Heid, Ulrich (Ed.), Proceedings of the 21st Conference on Natural Language Processing (KONVENS 2025): Workshops. HsH Applied Academics. ISSN 9783690180177. p. 104–119. doi: https:/aclanthology.org/2025.konvens-2.8.pdf. Full text in Research Archive Show summary

  This paper introduces a new dataset of document-level event annotations in the domain of armed conflict. By augmenting the event database from the Uppsala Conflict Data Program (UCDP) with source documents identified in public web archives, we create the UCDP Abstractive Event analysis Corpus (UCDP-AEC). While a large part of research on information extraction is focused on extracting text spans, realworld use cases often require inferring more high-level information that is not necessarily explicitly mentioned in texts. UCDP-AEC differs from traditional event extraction datasets in that the document-level annotations do not correspond to mere text spans of the input, but capture expert-interpreted and often implicit information. With more than 10 000 documents, UCDP-AEC is of comparable size to the largest human-annotated traditional event extraction datasets. We also report preliminary experimental results for various generative approaches, by fine-tuning both decoder models and existing event argument extraction models that require minimal adaptation to our abstractive formulation of the task

• Wold, Sondre; Charpentier, Lucas Georges Gabriel & Simon, Étienne (2025). Systematic Generalization in Language Models Scales with Information Entropy. In Che, Wanxiang; Nabende, Joyce; Shutova, Ekaterina & Pilehvar, Mohammad (Ed.), Findings of the Association for Computational Linguistics: ACL 2025. Association for Computational Linguistics (ACL). ISSN 9798891762565. p. 1807–1819. doi: 10.18653/v1/2025.findings-acl.90. Full text in Research Archive Show summary

  Systematic generalization remains challenging for current language models, which are known to be both sensitive to semantically similar permutations of the input and to struggle with known concepts presented in novel contexts. Although benchmarks exist for assessing compositional behavior, it is unclear how to measure the difficulty of a systematic generalization problem. In this work, we show how one aspect of systematic generalization can be described by the entropy of the distribution of component parts in the training data. We formalize a framework for measuring entropy in a sequence-to-sequence task and find that the performance of popular model architectures scales with the entropy. Our work connects systematic generalization to information efficiency, and our results indicate that success at high entropy can be achieved even without built-in priors, and that success at low entropy can serve as a target for assessing progress towards robust systematic generalization.

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