The importance of modeling pragmatic syntactic bootstrapping

Syntactic bootstrapping is based on the premise that there are probabilistic correspondences between the syntactic structure in which a word occurs and the word's meaning, and that such links hold, with some degree of generality, cross-linguistically. The procedure has been extensively discussed with respect to verbs, where it has been proposed as a mechanism for constraining the massive ambiguity that arises when inferring the meaning of a verb that is used to describe an event (Fisher, Hall, Rakowitz & Gleitman, 1994; Gleitman, 1990; Gleitman, Cassidy, Nappa, Papafragou & Trueswell, 2005). In her keynote paper (Hacquard, 2022), Hacquard focuses on classes of verbs for which inferences about meaning are arguably even harder, because they involve concepts that have no observable counterparts: these are attitude verbs such as think and want, and modals such as must and can. She walks us through, in meticulous detail, the limits of a purely syntactic bootstrapping mechanism, and she describes how augmenting syntactic information with pragmatic information, via pragmatic syntactic bootstrapping (Hacquard, 2022; Hacquard & Lidz, 2019), might address these limitations. The proposal is exciting, and the detail with which Hacquard works through these examples is impressive; she supports her arguments with behavioral experiments, corpus analyses, and two very targeted computational analyses. In this commentary I suggest that Hacquard's proposal is laid out in sufficient detail such that a comprehensive computational modeling effort would be fruitful for evaluating and further developing her account.