"We introduce the first end-to-end coreference resolution model and show that it significantly outperforms all previous work without using a syntactic parser or hand-engineered mention detector. The key idea is to directly consider all spans in a document as potential mentions and learn distributions over possible antecedents for each. The model computes span embeddings that combine context-dependent boundary representations with a head-finding attention mechanism. It is trained to maximize the marginal likelihood of gold antecedent spans from coreference clusters and is factored to enable aggressive pruning of potential mentions. Experiments demonstrate state-of-the-art performance, with a gain of 1.5 F1 on the OntoNotes benchmark and by 3.1 F1 using a 5-model ensemble, despite the fact that this is the first approach to be successfully trained with no external resources."
"We present the first state-of-the-art coreference resolution model that is learned end-to-end given only gold mention clusters. All recent coreference mdoels, including neural approaches that achieved impressive performance gains (Wiseman et al., 2016; Clark and Manning, 2016b,a), rely on syntactic parsers, both for head-word features and as the input to carefully hand-engineered mention proposal algorithms. We demonstrate for the first time that these resources are not required, and in fact performance can be improved significantly without them, by training an end-to-end neural model that jointly learns which spans are entity mentions and how to best cluster them.
"Our model reasons over the space of all spans up to a maximum length and directly optimizes the marginal likelihood of antecedent spans from gold coreference clusters. It