论文标题
代词核心分辨率的最新发展的简短调查和比较研究
A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution
论文作者
论文摘要
代词核心分辨率（PCR）是将代词表达式解决的任务。与一般的核心分辨率任务相比，PCR的主要挑战是核心关系预测而不是提及检测。作为一种重要的自然语言理解（NLU）组成部分，代词解决对于许多下游任务至关重要，对于现有模型仍然具有挑战性，这激励我们调查现有方法并考虑如何做得更好。在此调查中，我们首先介绍了代表性数据集和模型，以实现普通代词核心分辨率任务。然后，我们将重点放在Hard代表核心分辨率问题（例如Winograd模式挑战）上的最新进展，以分析当前模型能够理解常识的程度。我们进行了广泛的实验，以表明即使当前的模型在标准评估集中取得了良好的性能，但它们仍未准备好用于真实应用程序（例如，所有SOTA模型都在正确地将代词正确解析到罕见的对象上）。所有实验代码均可在https://github.com/hkust-knowcomp/pcr上找到。
Pronoun Coreference Resolution (PCR) is the task of resolving pronominal expressions to all mentions they refer to. Compared with the general coreference resolution task, the main challenge of PCR is the coreference relation prediction rather than the mention detection. As one important natural language understanding (NLU) component, pronoun resolution is crucial for many downstream tasks and still challenging for existing models, which motivates us to survey existing approaches and think about how to do better. In this survey, we first introduce representative datasets and models for the ordinary pronoun coreference resolution task. Then we focus on recent progress on hard pronoun coreference resolution problems (e.g., Winograd Schema Challenge) to analyze how well current models can understand commonsense. We conduct extensive experiments to show that even though current models are achieving good performance on the standard evaluation set, they are still not ready to be used in real applications (e.g., all SOTA models struggle on correctly resolving pronouns to infrequent objects). All experiment codes are available at https://github.com/HKUST-KnowComp/PCR.
