  The neural linear-chain CRF model is one of the most widely-used approach to
sequence labeling. In this paper, we investigate a series of increasingly
expressive potential functions for neural CRF models, which not only integrate
the emission and transition functions, but also explicitly take the
representations of the contextual words as input. Our extensive experiments
show that the decomposed quadrilinear potential function based on the vector
representations of two neighboring labels and two neighboring words
consistently achieves the best performance.
