A Noisy-Channel Model of Human Sentence Comprehension under Uncertain Input

Language comprehension, as with all other cases of the extraction of meaningful structure from perceptual input, takes places under noisy conditions. If human language comprehension is a rational process in the sense of making use of all available information sources, then we might expect uncertainty at the level of word-level input to affect sentence-level comprehension. However, nearly all contemporary models of sentence comprehension assume clean input—that is, that the input to the sentence-level comprehension mechanism is a perfectly-formed, completely certain sequence of input tokens (words). This article presents a simple model of rational human sentence comprehension under noisy input, and uses the model to investigate some outstanding problems in the psycholinguistic literature for theories of rational human sentence comprehension. We argue that by explicitly accounting for inputlevel noise in sentence processing, our model provides solutions for these outstanding problems and broadens the scope of theories of human sentence comprehension as rational probabilistic inference. ∗Part of this work has benefited from presentation at the 21st annual meeting of the CUNY Sentence Processing Conference in Chapel Hill, NC, 14 March 2008, and at a seminar at the Center for Research on Language, UC San Diego. I am grateful to Klinton Bicknell, Andy Kehler, and three anonymous reviewers for comments and suggestions, Cyril Allauzen for guidance regarding the OpenFST library, and to Mark Johnson, MarkJan Nederhof, and Noah Smith for discussion of renormalizing weighted CFGs.