Finding syntax in human encephalography with beam search
John Hale, Chris Dyer, Adhiguna Kuncoro, Jonathan Brennan · 2018
Recurrent neural network grammars (RNNGs) are generative models of (tree, string) pairs that rely on neural networks to evaluate derivational choices.Parsing with them using beam search yields a variety of incremental complexity metrics such as word surprisal and parser action count.When used as regressors against human electrophysiological responses to naturalistic text, they derive two amplitude effects: an early peak and a P600-like later peak.By contrast, a non-syntactic neural language model yields no reliable effects.Model comparisons attribute the early peak to syntactic composition within the RNNG.This pattern of results recommends the RNNG+beam search combination as a mechanistic model of the syntactic processing that occurs during normal human language comprehension.