Emergent Predication Structure in Hidden State Vectors of Neural Readers

Abstract

A significant number of neural architectures for reading comprehension have recently been developed and evaluated on large cloze-style datasets. We present experiments supporting the emergence of "predication structure" in the hidden state vectors of these readers. More specifically, we provide evidence that the hidden state vectors represent atomic formulas $\hat{I}¦[c]$ where $\hat{I}¦$ is a semantic property (predicate) and $c$ is a constant symbol entity identifier.

Accepted for Repl4NLP: 2nd Workshop on Representation Learning for NLP