Implicity Defined Neural Networks for Sequence Labeling
Abstract
In this work, we propose a novel, implicitly defined neural network architecture and describe a method to compute its components. The proposed architecture forgoes the causality assumption previously used to formulate recurrent neural networks and allow the hidden states of the network to coupled together, allowing potential improvement on problems with complex, long-distance dependencies. Initial experiments demonstrate the new architecture outperforms both the Stanford Parser and a baseline bidirectional network on the Penn Treebank Part-of-Speech tagging task and a baseline bidirectional network on an additional artificial random biased walk task.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 13, 2017
- Accession Number
- AD1028510
Entities
People
- Brian J. Thompson
- Michaeel M. Kazi
Organizations
- Massachusetts Institute of Technology