Potentially idiomatic expressions (PIEs) carry meanings inherently tied to the everyday experience of a given language community. As such, they constitute an interesting challenge for assessing the linguistic (and to some extent cultural) capabilities of NLP systems. In this paper, we present XMPIE, a parallel multilingual and multimodal dataset of potentially idiomatic expressions. The dataset, containing 34 languages and over ten thousand items, allows comparative analyses of idiomatic patterns among language-specific realisations and preferences in order to gather insights about shared cultural aspects. This parallel dataset allows evaluation of language model performance for a given PIE in different languages and whether idiomatic understanding in one language can be transferred to another. Moreover, the dataset supports the study of PIEs across textual and visual modalities, to measure to what extent PIE understanding in one modality transfers or implies in understanding in another modality (text vs. image). The data was created by language experts, with both textual and visual components crafted under multilingual guidelines, and each PIE is accompanied by five images representing a spectrum from idiomatic to literal meanings, including semantically related and random distractors. The result is a high-quality benchmark for evaluating multilingual and multimodal idiomatic language understanding.

This study explores the impact of annotation inconsistencies in Universal Dependencies (UD) treebanks on typological research in computational linguistics. UD provides a standardized framework for cross-linguistic annotation, facilitating large-scale empirical studies on linguistic diversity and universals. However, despite rigorous guidelines, annotation inconsistencies persist across treebanks. The objective of this paper is to assess how these inconsistencies affect typological universals, linguistic descriptions, and complexity metrics. We analyze systematic annotation errors in multiple UD treebanks, focusing on morphological features. Case studies on Spanish and Dutch demonstrate how differing annotation decisions within the same language create contradictory typological profiles. We classify the errors into two main categories: overgeneration errors (features incorrectly annotated, since do not actually exist in a language) and data omission errors (inconsistent or incomplete annotation of features that do exist). Our results show that these inconsistencies significantly distort typological analyses, leading to false generalizations and miscalculations of linguistic complexity. We propose methodological safeguards for typological research using UD data. Our findings highlight the need for methodological improvements to ensure more reliable cross-linguistic generalizations in computational typology.

In his Universal 14, Greenberg stated that the normal and dominant order in all world languages was to place the condition before the conclusion in conditional sentences. We take this claim to review it quantitatively and based on occurrences in real texts in more than 50 languages. We can see that Greenberg’s proposal is correct but that it needs a reformulation to be true at all. We propose a quantitatively based and updated Universal 14, which gives a better account of the representation of the different languages analyzed and which is fulfilled in 100% of the cases (as opposed to Greenberg’s 60% in our sample). In addition, we also analyze adverbial sentences. Once we obtain the occurrence data in their direction (before or after the main verb), we plot a new Universal in a typometrical way: 100% of the languages show a higher proportion of preceding conditional clauses than of adverbial clauses, regardless of their type or the direction preference for adverbial clauses. The relationship between the SOV type and a stricter initial conditional location is also proposed.