A fundamental result in psycholinguistics is that less predictable words take
a longer time to process. One theoretical explanation for this finding is
Surprisal Theory (Hale, 2001; Levy, 2008), which quantifies a word's
predictability as its surprisal, i.e. its negative log-probability given a
context. While evidence supporting the predictions of Surprisal Theory have
been replicated widely, most have focused on a very narrow slice of data:
native English speakers reading English texts. Indeed, no comprehensive
multilingual analysis exists. We address this gap in the current literature by
investigating the relationship between surprisal and reading times in eleven
different languages, distributed across five language families. Deriving
estimates from language models trained on monolingual and multilingual corpora,
we test three predictions associated with surprisal theory: (i) whether
surprisal is predictive of reading times; (ii) whether expected surprisal, i.e.
contextual entropy, is predictive of reading times; (iii) and whether the
linking function between surprisal and reading times is linear. We find that
all three predictions are borne out crosslinguistically. By focusing on a more
diverse set of languages, we argue that these results offer the most robust
link to-date between information theory and incremental language processing
across languages.

通过对 11 种不同语言进行研究，我们发现惊奇理论与阅读时间之间存在跨语言的联系，验证了三个预测：惊奇度是否预测阅读时间、预期惊奇度是否预测阅读时间、以及惊奇度与阅读时间之间的联系是否是线性的，从而在更多语言上提供了信息论与递增语言处理之间至今最强大的连接。