What statistical properties might support learning abstract grammatical knowledge from linear input?We address this question by examining the statistical distribution of function words.Function words have been argued to aid acquisition through three distributional properties: high frequency, reliable syntactic association, and phrase-boundary alignment.We conduct a cross-linguistic corpus analysis of 186 languages, which confirms that all three properties are universal.Using counterfactual language modeling and ablation experiments on English, we show that preserving these properties facilitates acquisition in neural learners, with a Goldilocks effect: function words must be frequent enough to be reliable, yet diverse enough to remain informative to structural dependency.Probing analyses further reveal that different learning conditions produce systematically different reliance on function words. 1

Francesca Padovani, Xiulin Yang, Bastian Bunzeck, Jaap Jumelet, Yevgen Matusevych, Nathan Schneider, Arianna Bisazza. Proceedings of the 30th Conference on Computational Natural Language Learning. 2026.

Do language models (LMs) offer insights into human language learning?A common argument against this idea is that because their architecture and training paradigm are so vastly different from humans, LMs can learn arbitrary inputs as easily as natural languages.We test this claim by training LMs to model impossible and typologically unattested languages.Unlike previous work, which has focused exclusively on English, we conduct experiments on 12 languages from 4 language families with two newly constructed parallel corpora.Our results show that while GPT-2 small can largely distinguish attested languages from their impossible counterparts, it does not achieve perfect separation between all the attested languages and all the impossible ones.We further test whether GPT-2 small distinguishes typologically attested from unattested languages with different NP orders by manipulating word order based on Greenberg's Universal 20.We find that the model's perplexity scores do not distinguish attested vs. unattested word orders, while its performance on the generalization test does.These findings suggest that LMs exhibit some human-like inductive biases, though these biases are weaker than those found in human learners.

This paper explores whether language models can effectively resolve the complex binding patterns of the Mandarin Chinese reflexive ziji, which are constrained by both syntactic and semantic factors. We construct a dataset of 320 synthetic sentences using templates and examples from syntactic literature, along with 360 natural sentences from the BCC corpus. Evaluating 21 language models against this dataset and comparing their performance to judgments from native Mandarin speakers, we find that none of the models consistently replicates human-like judgments. The results indicate that existing language models tend to rely heavily on sequential cues, though not always favoring the closest strings, and often overlooking subtle semantic and syntactic constraints. They tend to be more sensitive to noun-related than verb-related semantics.

We present the first comprehensive set of guidelines for German Abstract Meaning Representation (Deutsche AMR, DeAMR) along with an annotated corpus of 400 DeAMR. Taking English AMR (EnAMR) as our starting point, we propose significant adaptations to faithfully represent the structure and semantics of German, focusing particularly on verb frames, compound words, and modality. We validate our annotation through inter-annotator agreement and further evaluate our corpus with a comparison of structural divergences between EnAMR and DeAMR on parallel sentences, replicating previous work that finds both cases of cross-lingual structural alignment and cases of meaningful linguistic divergence. Finally, we fine-tune state-of-the-art multi-lingual and cross-lingual AMR parsers on our corpus and find that, while our small corpus is insufficient to produce quality output, there is a need to continue develop and evaluate against gold non-English AMR data.

Discourse Representation Theory (DRT) distinguishes itself from other semantic representation frameworks by its ability to model complex semantic and discourse phenomena through structural nesting and variable binding.While seq2seq models hold the state of the art on DRT parsing, their accuracy degrades with the complexity of the sentence, and they sometimes struggle to produce well-formed DRT representations.We introduce the AMS parser, a compositional, neurosymbolic semantic parser for DRT.It rests on a novel mechanism for predicting quantifier scope.We show that the AMS parser reliably produces well-formed outputs and performs well on DRT parsing, especially on complex sentences.1