Although recent end-to-end neural coreference models have advanced the state-of-the-art performance for coreference resolution, they are still trained with heuristic loss functions and make a sequence of local decisions for each pair of mentions.
overall, our full model achieves the state-ofthe-art performance of 73.8% F1-score when using ELMo and entropy regularization (compared to models marked with * in Table 1), and our approach simultaneously obtains the best F1-score of 70.5% when using fixed word embedding only.