Integrating surprisal and uncertain-input models in online sentence comprehension: formal techniques and empirical results

Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pages 1055–1065, Portland, Oregon, June 19-24, 2011. c⃝2011 Association for Computational Linguistics Integrating surprisal and uncertain-input models in online sentence comprehension: formal techniques and empirical results Roger Levy Department of Linguistics University of California at San Diego 9500 Gilman Drive # 0108 La Jolla, CA 92093-0108 rlevy@ucsd.edu Abstract A system making optimal use of available in- formation in incremental language compre- hension might be expected to use linguistic knowledge together with current input to re- vise beliefs about previous input. Under some circumstances, such an error-correction capa- bility might induce comprehenders to adopt grammatical analyses that are inconsistent with the true input. Here we present a for- mal model of how such input-unfaithful gar- den paths may be adopted and the difficulty incurred by their subsequent disconfirmation, combining a rational noisy-channel model of syntactic comprehension under uncertain in- put with the surprisal theory of incremental processing difficulty. We also present a behav- ioral experiment confirming the key empirical predictions of the theory. 1 Introduction In most formal theories of human sentence compre- hension, input recognition and syntactic analysis are taken to be distinct processes, with the only feed- back from syntax to recognition being prospective prediction of likely upcoming input (Jurafsky, 1996; Narayanan and Jurafsky, 1998, 2002; Hale, 2001, 2006; Levy, 2008a). Yet a system making optimal use of all available information might be expected to perform fully joint inference on sentence identity and structure given perceptual input, using linguistic knowledge both prospectively and retrospectively in drawing inferences as to how raw input should be segmented and recognized as a sequence of linguis- tic tokens, and about the degree to which each input token should be trusted during grammatical analysis. Formal models of such joint inference over uncer- tain input have been proposed (Levy, 2008b), and corroborative empirical evidence exists that strong coherence of current input with a perceptual neigh- bor of previous input may induce confusion in com- prehenders as to the identity of that previous input (Connine et al., 1991; Levy et al., 2009). In this paper we explore a more dramatic predic- tion of such an uncertain-input theory: that, when faced with sufficiently biasing input, comprehen- ders might under some circumstances adopt a gram- matical analysis inconsistent with the true raw in- put comprising a sentence they are presented with, but consistent with a slightly perturbed version of the input that has higher prior probability. If this is the case, then subsequent input strongly disconfirm- ing this “hallucinated” garden-path analysis might be expected to induce the same effects as seen in classic cases of garden-path disambiguation tradi- tionally studied in the psycholinguistic literature. We explore this prediction by extending the ratio- nal uncertain-input model of Levy (2008b), integrat- ing it with SURPRISAL THEORY (Hale, 2001; Levy, 2008a), which successfully accounts for and quan- tifies traditional garden-path disambiguation effects; and by testing predictions of the extended model in a self-paced reading study. Section 2 reviews surprisal …

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