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Can Entropy Explain Successor Surprisal Effects in Reading?

機器翻譯熵可以解釋閱讀中的后繼驚人效應嗎?

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【摘要】Human reading behavior is sensitive to surprisal: more predictable words tend to be read faster. Unexpectedly, this applies not only to the surprisal of the word that is currently being read, but also to the surprisal of upcoming (successor) words that have not been fixated yet. This finding has been interpreted as evidence that readers can extract lexical information parafoveally. Calling this interpretation into question, Angele et al. (2015) showed that successor effects appear even in contexts in which those successor words are not yet visible. They hypothesized that successor surprisal predicts reading time because it approximates the reader's uncertainty about upcoming words. We test this hypothesis on a reading time corpus using an LSTM language model, and find that successor surprisal and entropy are independent predictors of reading time. This independence suggests that entropy alone is unlikely to be the full explanation for successor surprisal effects.

【作者】Marten van Schijndel; Tal Linzen;

【作者單位】Department of Cognitive Science Johns Hopkins University; Department of Cognitive Science Johns Hopkins University;

【年(卷),期】2019,,

【頁碼】1-7

【總頁數】7

【正文語種】eng

【中圖分類】;

【關鍵詞】;


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