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.

中文翻译：

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了解提及检测器-链接器交互以实现神经共指解析

共指消解是语篇级自然语言理解的一项重要任务。然而，尽管最近取得了重大进展，但当前最先进系统的质量仍然远远落后于人类水平。使用 CoNLL-2012 和 PreCo 数据集，我们剖析了作为当前大多数性能最佳的共指系统基础的主流端到端共指解析模型的最佳实例，并凭经验分析了其两个组件的行为：提及检测器和提及链接器。虽然检测器传统上非常注重召回作为设计决策，但我们证明了精度的重要性，要求它们保持平衡。然而，我们指出，由于无法做出重要的照应判断，构建精确的检测器很困难。我们还强调了改进链接器的巨大空间，其余错误主要涉及代词解析。我们希望我们的发现将有助于未来构建共指解析系统的研究。