  Conditional random fields (CRFs) have been shown to be one of the most
successful approaches to sequence labeling. Various linear-chain neural CRFs
(NCRFs) are developed to implement the non-linear node potentials in CRFs, but
still keeping the linear-chain hidden structure. In this paper, we propose NCRF
transducers, which consists of two RNNs, one extracting features from
observations and the other capturing (theoretically infinite) long-range
dependencies between labels. Different sequence labeling methods are evaluated
over POS tagging, chunking and NER (English, Dutch). Experiment results show
that NCRF transducers achieve consistent improvements over linear-chain NCRFs
and RNN transducers across all the four tasks, and can improve state-of-the-art
results.
