Extending dependencies to the taggedPBC: Word order in transitive clauses

The taggedPBC (Ring 2025a) contains more than 1,800 sentences of pos-tagged parallel text data from over 1,500 languages, representing 133 language families and 111 isolates. While this dwarfs previously available resources, and the POS tags achieve decent accuracy, allowing for predictive crosslinguistic insights (Ring 2025b), the dataset was not initially annotated for dependencies. This paper reports on a CoNLLU-formatted version of the dataset which transfers dependency information along with POS tags to all languages in the taggedPBC. Although there are various concerns regarding the quality of the tags and the dependencies, word order information derived from this dataset regarding the position of arguments and predicates in transitive clauses correlates with expert determinations of word order in three typological databases (WALS, Grambank, Autotyp). This highlights the usefulness of corpus-based typological approaches (as per Baylor et al. 2023; Bjerva 2024) for extending comparisons of discrete linguistic categories, and suggests that important insights can be gained even from noisy data, given sufficient annotation. The dependency-annotated corpora are also made available for research and collaboration via GitHub.

Word length predicts word order: "Min-max"-ing drives language evolution

Current theories of language propose an innate (Baker 2001; Chomsky 1981) or a functional (Greenberg 1963; Dryer 2007; Hawkins 2014) origin for the surface structures (i.e. word order) that we observe in languages of the world, while evolutionary modeling (Dunn et al. 2011) suggests that descent is the primary factor influencing such patterns. Although there are hypotheses for word order change from both innate and usage-based perspectives for specific languages and families, there are key disagreements between the two major proposals for mechanisms that drive the evolution of language more broadly (Wasow 2002; Levy 2008). This paper proposes a universal underlying mechanism for word order change based on a large tagged parallel dataset of over 1,500 languages representing 133 language families and 111 isolates. Results indicate that word class length is significantly correlated with word order crosslinguistically, but not in a straightforward manner, partially supporting opposing theories of processing, while at the same time predicting historical word order change in two different phylogenetic lines and explaining more variance than descent or language area in regression models. Such findings suggest an integrated "Min-Max" theory of language evolution driven by competing pressures of processing and information structure, aligning with recent efficiency-oriented (Levshina 2023) and information-theoretic proposals (Zaslavsky 2020; Tucker et al. 2025).

The taggedPBC: Annotating a massive parallel corpus for crosslinguistic investigations

Existing datasets available for crosslinguistic investigations have tended to focus on large amounts of data for a small group of languages or a small amount of data for a large number of languages. This means that claims based on these datasets are limited in what they reveal about universal properties of the human language faculty. While this has begun to change through the efforts of projects seeking to develop tagged corpora for a large number of languages, such efforts are still constrained by limits on resources. The current paper reports on a large automatically tagged parallel dataset which has been developed to partially address this issue. The taggedPBC contains more than 1,800 sentences of pos-tagged parallel text data from over 1,500 languages, representing 133 language families and 111 isolates, dwarfing previously available resources. The accuracy of tags in this dataset is shown to correlate well with both existing SOTA taggers for high-resource languages (SpaCy, Trankit) as well as hand-tagged corpora (Universal Dependencies Treebanks). Additionally, a novel measure derived from this dataset, the N1 ratio, correlates with expert determinations of word order in three typological databases (WALS, Grambank, Autotyp) such that a Gaussian Naive Bayes classifier trained on this feature can accurately identify basic word order for languages not in those databases. While much work is still needed to expand and develop this dataset, the taggedPBC is an important step to enable corpus-based crosslinguistic investigations, and is made available for research and collaboration via GitHub.