Kushal Tatariya, Artur Kulmizev, Wessel Poelman, Esther Ploeger, Marcel Bollmann, Johannes Bjerva, Jiaming Luo, Heather Lent, Miryam de Lhoneux. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

The extent to which individual language characteristics influence tokenization and language modeling is an open question.Differences in morphological systems have been suggested as both unimportant and crucial to consider (Cotterell et al., 2018; Gerz et al., 2018a; Park et al., 2021, inter alia).We argue this conflicting evidence is due to confounding factors in experimental setups, making it hard to compare results and draw conclusions.We identify such factors in analyses trying to answer the question of whether, and how, morphology relates to language modeling.Next, we re-assess three hypotheses by Arnett and Bergen (2025) for why modeling agglutinative languages results in higher perplexities than fusional languages: they look at morphological alignment of tokenization, tokenization efficiency, and dataset size.We show that each conclusion includes confounding factors and suggest methodological improvements.Finally, we introduce token bigram metrics as an intrinsic way to predict the difficulty of causal language modeling, and find that they are gradient proxies for morphological complexity that do not require expert annotation.Ultimately, we outline necessities to reliably answer whether, and how, morphology relates to language modeling.

The NLP research community has devoted increased attention to languages beyond English, resulting in considerable improvements for multilingual NLP.However, these improvements only apply to a small subset of the world's languages.An increasing number of papers aspires to enhance generalizable multilingual performance across languages.To this end, linguistic typology is commonly used to motivate language selection, on the basis that a broad typological sample ought to imply generalization across a broad range of languages.These selections are often described as being 'typologically diverse'.In this meta-analysis, we systematically investigate NLP research that includes claims regarding typological diversity.We find there are no set definitions or criteria for such claims.We introduce metrics to approximate the diversity of resulting language samples along several axes and find that the results vary considerably across papers.Crucially, we show that skewed language selection can lead to overestimated multilingual performance.We recommend future work to include an operationalization of typological diversity that empirically justifies the diversity of language samples.To help facilitate this, we release the code for our diversity measures.1 * Equal contribution. 1 Our code and data are publicly available: https://github.com/WPoelman/typ-div

Even though many recent semantic parsers are based on deep learning methods, we should not forget that rule-based alternatives might offer advantages over neural approaches with respect to transparency, portability, and explainability. Taking advantage of existing off-the-shelf Universal Dependency parsers, we present a method that maps a syntactic dependency tree to a formal meaning representation based on Discourse Representation Theory. Rather than using lambda calculus to manage variable bindings, our approach is novel in that it consists of using a series of graph transformations. The resulting UD semantic parser shows good performance for English, German, Italian and Dutch, with F-scores over 75%, outperforming a neural semantic parser for the lower-resourced languages. Unlike neural semantic parsers, our UD semantic parser does not hallucinate output, is relatively easy to port to other languages, and is completely transparent.