Predicting wh-dependencies: Parsing, interpretation, and learning perspectives – Akira Omaki (Johns Hopkins University)
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This talk focuses on syntactic prediction and examines its implication for models of sentence processing and language learning. Predicting upcoming syntactic structures reduces processing demand and allows successful comprehension in the presence of noise, but on the other hand, such predictions are risky in that they could potentially lead readers/listeners to wrong analyses (i.e. garden-paths) and cause processing difficulties that we often fail to overcome. The goal of the talk is three-fold. First, I will present a series of eye-tracking data with adults to establish that predictive syntactic analyses and interpretations are indeed possible in processing wh-dependencies. Second, I will examine the risks of wh-dependency prediction for adults and children. The first risk factor is revision failure: the failure to revise the initial analysis can lead adults and children to misunderstand sentences with wh-dependencies. Here, I will present comprehension data on adults and children’s revision failures in French, English and Japanese and demonstrate that the degree of revision difficulties can be attenuated by semantic properties of the verbs. The second risk factor is consequence on learning: If learners predictively analyze wh-dependencies and always disambiguate the dependencies with a bias, would the input distribution for learners be skewed in such a way that the learners fail to observe certain interpretive possibilities that are allowed in the target language? I will discuss the distribution of wh-dependencies in child-directed speech, and examine how the input distribution will be skewed when we incorporate the experimental findings on children’s parsers. I will argue that a simple integration of syntactic prediction could potentially create a learnability problem, but that this problem could be overcome once we allow children to integrate verb information to reanalyze the parse.
Akira Omaki is an assistant professor of Cognitive Science at the Johns Hopkins University, and his research focuses on the dynamics of sentence processing and first/second language development. He received his PhD in Linguistics at the University of Maryland, and joined the Cognitive Science faculty after a post-doc at the University of Geneva.