论文标题
Lingmess：语言知情的多专家得分手，以解决核心分辨率
LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution
论文作者
论文摘要
虽然Coreference分辨率通常涉及各种语言挑战，但最近的模型基于所有类型对的单个成对得分手。我们提出了LingMess，这是一种新的核心模型，该模型定义了不同类别的核心案例并优化了多个成对得分手，每个得分手都会学习一组特定的语言挑战。我们的模型大大提高了大多数类别的成对得分，并且在Ontonotes和5个附加数据集上的群集级别的性能优于群集级别。我们的模型可在https://github.com/shon-otmazgin/lingmess-coref中找到
While coreference resolution typically involves various linguistic challenges, recent models are based on a single pairwise scorer for all types of pairs. We present LingMess, a new coreference model that defines different categories of coreference cases and optimize multiple pairwise scorers, where each scorer learns a specific set of linguistic challenges. Our model substantially improves pairwise scores for most categories and outperforms cluster-level performance on Ontonotes and 5 additional datasets. Our model is available in https://github.com/shon-otmazgin/lingmess-coref
