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The key idea is to directly consider all\nspans in a document as potential mentions and learn distributions over possible\nantecedents for each. The model computes span embeddings that combine\ncontext-dependent boundary representations with a head-finding attention\nmechanism. It is trained to maximize the marginal likelihood of gold antecedent\nspans from coreference clusters and is factored to enable aggressive pruning of\npotential mentions. 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