论文标题
理解提及神经核心分辨率中的检测器链互动
Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution
论文作者
论文摘要
尽管最近的核心解决方案最近取得了重大进展，但当前最新系统的质量仍在落后于人类水平的性能。使用CONLL-2012和PROCO数据集，我们剖析了主流端到端核心分辨率模型的最佳实例化，该模型是最新表现最佳的核心系统的基础，并经验分析了其两个组件的行为：提及检测器和提及链接器。尽管探测器传统上专注于召回作为设计决定，但我们证明了精确的重要性，要求其平衡。但是，我们指出，由于无法做出重要的放置决策，难以建立精确的检测器。我们还强调了改善链接器的巨大空间，并表明其其余错误主要涉及代词分辨率。我们提出了有希望的下一步，并希望我们的发现将有助于将来的核心解决方案研究。
Despite significant recent progress in coreference resolution, 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: 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 show that the rest of its errors mainly involve pronoun resolution. We propose promising next steps and hope our findings will help future research in coreference resolution.
