CCG Supertagging with a Recurrent Neural Network

Recent work on supertagging using a feedforward neural network achieved significant improvements for CCG supertagging and parsing (Lewis and Steedman, 2014). However, their architecture is limited to considering local contexts and does not naturally model sequences of arbitrary length. In this paper, we show how directly capturing sequence information using a recurrent neural network leads to further accuracy improvements for both su

• Type: Journal
• Year: 2015
• Where: ACL
• Authors: Wenduan Xu, Michael Auli, Stephen Clark
• Keywords: ACL 2015, Computational Linguistics