Modelling Reading Times in Bilingual Sentence Comprehension

Stefan FrankRadboud University Nijmegen

Abstract

Relatively little is known about the interaction between a bilingual's two languages beyond the word level. This paper investigates the issue by comparing word reading times (RTs) in both L1 and L2 to quantitative predictions by statistical language models. Recurrent neural networks are trained on either a Dutch corpus, an English corpus, or the two corpora combined (i.e., the bilingual network treats the two languages as one). Next, estimates of word surprisal by the three models are compared to RTs by native Dutch speakers on L1 Dutch and L2 English sentences. The monolingual Dutch model accounts for RTs on Dutch better than the bilingual model. In contrast, the bilingual model outperforms the monolingual English model on English RTs. These findings suggest that sentence comprehension in L1 is not much affected by L2 knowledge, whereas L2 reading does show interference from L1.

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