Human Sentence Processing: Recurrence or Attention?
This challenges the widely held idea that human sentence processing involves recurrent and immediate processing, providing evidence for cue-based retrieval, which is significant for cognitive science and computational linguistics.
The study tackled the problem of modeling human sentence processing by comparing Transformer- and RNN-based language models, finding that Transformers outperform RNNs in explaining self-paced reading times and neural activity during reading English sentences.
Recurrent neural networks (RNNs) have long been an architecture of interest for computational models of human sentence processing. The recently introduced Transformer architecture outperforms RNNs on many natural language processing tasks but little is known about its ability to model human language processing. We compare Transformer- and RNN-based language models' ability to account for measures of human reading effort. Our analysis shows Transformers to outperform RNNs in explaining self-paced reading times and neural activity during reading English sentences, challenging the widely held idea that human sentence processing involves recurrent and immediate processing and provides evidence for cue-based retrieval.