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A central tenet of cognitive linguistics is that adults’ knowledge of language consists of a structured inventory of constructions, including various two-argument constructions such as the active (e.g.,  Lizzy rescued John ), the passive (e.g.,  John was rescued by Lizzy ) and “fronting” constructions (e.g.,  John was the one Lizzy rescued) . But how do speakers choose which construction to use for a particular utterance, given constraints such as discourse/information structure and the semantic fit between verb and construction? The goal of the present study was to build a computational model of this phenomenon for two-argument constructions in Mandarin. First, we conducted a grammaticality judgment study with 60 native speakers which demonstrated that, across 57 verbs, semantic affectedness – as determined by further 16 native speakers – predicted each verb’s relative acceptability in the  bei -passive and  ba -active constructions, but not the Notional Passive and SVO Active constructions. Second, in order to simulate acquisition of these competing constraints, we built a computational model that learns to map from corpus-derived input (information structure + verb semantics + lexical verb identity) to an output representation corresponding to these four constructions (+“other”). The model was able to predict judgments of the relative acceptability of the test verbs in the  ba -active and  bei -passive constructions obtained in Study 1, with model-human correlations in the region of  r  = 0.5 and  r  = 0.3, respectively. Surprisingly, these correlations increased (to  r  = 0.75 and  r  = 0.5 respectively) when lexical verb identity was removed; perhaps because this information leads to over-fitting of the training set. These findings suggest the intriguing possibility that acquiring constructions involves forgetting as a mechanism for abstracting across certain fine-grained lexical details and idiosyncrasies.

cognitive linguistics

Balancing information-structure and semantic constraints on construction choice: building a computational model of passive and passive-like constructions in Mandarin Chinese