Higher-order Coreference Resolution with Coarse-to-fine Inference
Abstract - We introduce a fully differentiable approxima-tion to higher-order inference for coreferenceresolution. Our approach uses the antecedentdistribution from a span-ranking architectureas an attention mechanism to iteratively re-fine span representations. This enables themodel to softly consider multiple hops in thepredicted clusters. To alleviate the computa-tional cost of this iterative process, we intro-duce a coarse-to-fine approach that incorpo-rates a less accurate but more efficient bilin-ear factor, enabling more aggressive pruningwithout hurting accuracy. Compared to the ex-isting state-of-the-art span-ranking approach,our model significantly improves accuracy onthe English OntoNotes benchmark, while be-ing far more computationally efficient
Paper - https://arxiv.org/pdf/1804.05392.pdf
Dataset - https://github.com/kentonl/e2e-coref