Gradient Grammaticality in Model-Theoretic Dependency Grammar

Generative linguistics typically treats grammaticality as a binary matter, attributing the gradient effects frequently observed in acceptability judgment experiments to performance factors rather than to the grammar. Yet no specific performance account has, to our knowledge, been offered for most such effects, which strengthens the case for gradient grammaticality. Gradient judgments, however, have received little systematic study in experimental syntax, and the frameworks built on existing findings are better characterized as blueprints than formalized grammars. This paper takes a step towards closing both gaps. Four acceptability judgment experiments, two in English and two in Polish, manipulated violations of two constraints: subject–verb agreement and syntactic selectional restrictions. The results show that violation costs are systematically gradient, differing by constraint and by predicate, and reliably cumulative in that two violations are generally worse than one. Crucially, however, they combine under-additively rather than by linear summation as commonly assumed. We then outline Gradient Model-Theoretic Dependency Grammar, a formally explicit, monostratal, constraint-based framework in which constraints are adorned with stochastic penalty weights and violations combine through an under-additive function. As a proof of concept, we apply it to one of our experiments to estimate weights from the judgment data and derive acceptability predictions.