Character-based Surprisal as a Model of Reading Difficulty in the Presence of Errors
- Michael Hahn, Linguistics, Stanford University, Stanford, California, United States
- Frank Keller, University of Edinburgh, Edinburgh, United Kingdom
- Yonatan Bisk, Paul G. Allen School of Computer Sci & Eng, University of Washington, Seattle, Washington, United States
- Yonatan Belinkov, Harvard University, Cambridge, Massachusetts, United States
AbstractIntuitively, human readers cope easily with errors in text; typos, misspelling, word substitutions, etc. do not unduly disrupt natural reading. Previous work indicates that letter transpositions result in increased reading times, but it is unclear if this effect generalizes to more natural errors. In this paper, we report an eye-tracking study that compares two error types (letter transpositions and naturally occurring misspelling) and two error rates (10 % or 50 % of all words contain errors). We find that human readers show unimpaired comprehension in spite of these errors, but error words cause more reading difficulty than correct words. Also, transpositions are more difficult than misspellings, and a high error rate increases difficulty for all words, including correct ones. We then present a computational model that uses character-based (rather than traditional word-based) surprisal to account for these results. The model explains that transpositions are harder than misspellings because they contain unexpected letter combinations. It also explains the error rate effect: upcoming words are more difficult to predict when the context is degraded, leading to increased surprisal.
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