Pre- and in-parsing models for neural empty category detection

Abstract

Motivated by the positive impact of empty categories on syntactic parsing, we study neural models for pre- and in-parsing detection of empty categories, which has not previously been investigated. We find several non-obvious facts: (a) BiLSTM can capture non-local contextual information which is essential for detecting empty categories, (b) even with a BiLSTM, syntactic information is still able to enhance the detection, and (c) automatic detection of empty categories improves parsing quality for overt words. Our neural ECD models outperform the prior state-of-the-art by significant margins.