Coreference resolution is an important task for discourse-level natural language understanding. However, despite significant recent progress, the quality of current state-of-the-art systems still considerably trails behind human-level performance. Using the CoNLL-2012 and PreCo datasets, we dissect the best instantiation of the mainstream end-to-end coreference resolution model that underlies most current best-performing coreference systems, and empirically analyze the behavior of its two components: the mention detector and mention linker. While the detector traditionally focuses heavily on recall as a design decision, we demonstrate the importance of precision, calling for their balance. However, we point out the difficulty in building a precise detector due to its inability to make important anaphoricity decisions. We also highlight the enormous room for improving the linker and that the rest of its errors mainly involve pronoun resolution. We hope our findings will help future research in building coreference resolution systems.

本研究探讨了现有最佳表现的指代消解系统的端到端核心指代消解模型的两个组件：提及检测器和提及链接器，并分析它们的行为，强调了精度和召回率的平衡问题，以及精度检测器的困难以及链接器的改进空间，提出了有前途的下一步研究方向。

神经指代消解中提及探测器和链接器交互的理解