  Despite successful applications across a broad range of NLP tasks,
conditional random fields ("CRFs"), in particular the linear-chain variant, are
only able to model local features. While this has important benefits in terms
of inference tractability, it limits the ability of the model to capture
long-range dependencies between items. Attempts to extend CRFs to capture
long-range dependencies have largely come at the cost of computational
complexity and approximate inference. In this work, we propose an extension to
CRFs by integrating external memory, taking inspiration from memory networks,
thereby allowing CRFs to incorporate information far beyond neighbouring steps.
Experiments across two tasks show substantial improvements over strong CRF and
LSTM baselines.
