We evaluate a rule-based (Lee et al., 2013) and neural (Lee et al., 2018)
coreference system on Dutch datasets of two domains: literary novels and
news/Wikipedia text. The results provide insight into the relative strengths of
data-driven and knowledge-driven systems, as well as the influence of domain,
document length, and annotation schemes. The neural system performs best on
news/Wikipedia text, while the rule-based system performs best on literature.
The neural system shows weaknesses with limited training data and long
documents, while the rule-based system is affected by annotation differences.
The code and models used in this paper are available at
this https URL

本研究在荷兰文学小说和新闻 / 维基百科文本领域评估了基于规则 (Lee 等人，2013) 和基于神经网络 (Lee 等人，2018) 的共指系统。结果揭示了数据驱动和知识驱动系统的相对优势，以及领域、文档长度和注释方案的影响。神经系统在新闻 / 维基百科文本上表现最佳，而规则系统在文学方面表现最佳。

荷兰小说和新闻中基于规则和神经网络的指代消解基准测试

A Benchmark of Rule-Based and Neural Coreference Resolution in Dutch  Novels and News

In this paper, we quantify, analyze and mitigate gender bias exhibited in
ELMo's contextualized word vectors. First, we conduct several intrinsic
analyses and find that (1) training data for ELMo contains significantly more
male than female entities, (2) the trained ELMo embeddings systematically
encode gender information and (3) ELMo unequally encodes gender information
about male and female entities. Then, we show that a state-of-the-art
coreference system that depends on ELMo inherits its bias and demonstrates
significant bias on the WinoBias probing corpus. Finally, we explore two
methods to mitigate such gender bias and show that the bias demonstrated on
WinoBias can be eliminated.

本文中，我们量化、分析和减轻了 ELMo 上表现的性别偏见。通过内在分析和实验，我们发现 ELMo 的训练数据中包含男性实体比女性实体多，其编码的词向量中存在性别信息，且男性和女性实体所包含的性别信息不平等。同时，我们发现最前沿的指代消解系统很大程度上依赖 ELMo，继承了 ELMo 的性别偏见，表现出显著的偏见，我们探索了两种减轻这种性别偏见的方法，并展示了这种在 WinoBias 数据集上表现出的偏见可以被消除。