Syntax has been shown to benefit Coreference Resolution from incorporating
long-range dependencies and structured information captured by syntax trees,
either in traditional statistical machine learning based systems or recently
proposed neural models. However, most leading systems use only dependency
trees. We argue that constituent trees also encode important information, such
as explicit span-boundary signals captured by nested multi-word phrases, extra
linguistic labels and hierarchical structures useful for detecting anaphora. In
this work, we propose a simple yet effective graph-based method to incorporate
constituent syntactic structures. Moreover, we also explore to utilise
higher-order neighbourhood information to encode rich structures in constituent
trees. A novel message propagation mechanism is therefore proposed to enable
information flow among elements in syntax trees. Experiments on the English and
Chinese portions of OntoNotes 5.0 benchmark show that our proposed model either
beats a strong baseline or achieves new state-of-the-art performance. (Code is
available at this https URL)

通过使用基于图的方法，利用句法结构中的成分结构，包括语法树的长距离依赖和结构信息，实现了较强的代词消解技术。实验结果显示，在 OntoNotes 5.0 基准测试的英语和中文部分中，提出的模型要么超过了强基线要么实现了新的最高水平。

利用成分句法进行共指消解

Incorporating Constituent Syntax for Coreference Resolution

Multi-task learning (MTL) allows deep neural networks to learn from related
tasks by sharing parameters with other networks. In practice, however, MTL
involves searching an enormous space of possible parameter sharing
architectures to find (a) the layers or subspaces that benefit from sharing,
(b) the appropriate amount of sharing, and (c) the appropriate relative weights
of the different task losses. Recent work has addressed each of the above
problems in isolation. In this work we present an approach that learns a latent
multi-task architecture that jointly addresses (a)--(c). We present experiments
on synthetic data and data from OntoNotes 5.0, including four different tasks
and seven different domains. Our extension consistently outperforms previous
approaches to learning latent architectures for multi-task problems and
achieves up to 15% average error reductions over common approaches to MTL.

本文提出了一种基于潜在多任务体系结构的方法，该方法可以在 OntoNotes 5.0 的人造数据和数据中的多种领域和四种不同的任务中，比以往任何一种学习潜在体系结构的多任务问题方法都表现得更优秀，可以实现高达 15% 的平均误差降低率。