A Neural Model of Adaptation in Reading

Abstract

It has been argued that humans rapidly adapt their lexical and syntactic expectations to match the statistics of the current linguistic context. We provide further support to this claim by showing that the addition of a simple adaptation mechanism to a neural language model improves our predictions of human reading times compared to a non-adaptive model. We analyze the performance of the model on controlled materials from psycholinguistic experiments and show that it adapts not only to lexical items but also to abstract syntactic structures.

Accepted for presentation at EMNLP 2018 Updated with final camera-ready version (more colorblind-friendly, better catastrophic forgetting eval)