In this paper, we pose the question: do people talk about women and men in
different ways? We introduce two datasets and a novel integration of approaches
for automatically inferring gender associations from language, discovering
coherent word clusters, and labeling the clusters for the semantic concepts
they represent. The datasets allow us to compare how people write about women
and men in two different settings - one set draws from celebrity news and the
other from student reviews of computer science professors. We demonstrate that
there are large-scale differences in the ways that people talk about women and
men and that these differences vary across domains. Human evaluations show that
our methods significantly outperform strong baselines.

本研究通过语言自动推断性别关联发现单词聚类，并针对其所代表的语义概念进行标记，并比较了两个不同领域写作中人们如何谈论妇女和男子之间的差异，证明了人们谈论妇女和男子的方式存在大规模差异，并且这些差异在不同领域中有所不同。

从语言中自动推断性别联想

Automatically Inferring Gender Associations from Language

We describe a simple but effective method for cross-lingual syntactic
transfer of dependency parsers, in the scenario where a large amount of
translation data is not available. The method makes use of three steps: 1) a
method for deriving cross-lingual word clusters, which can then be used in a
multilingual parser; 2) a method for transferring lexical information from a
target language to source language treebanks; 3) a method for integrating these
steps with the density-driven annotation projection method of Rasooli and
Collins (2015). Experiments show improvements over the state-of-the-art in
several languages used in previous work, in a setting where the only source of
translation data is the Bible, a considerably smaller corpus than the Europarl
corpus used in previous work. Results using the Europarl corpus as a source of
translation data show additional improvements over the results of Rasooli and
Collins (2015). We conclude with results on 38 datasets from the Universal
Dependencies corpora.

本文提出了一种简单而有效的方法，可以在没有大量翻译数据的情况下进行跨语言的句法转移，这种方法基于三个步骤：1）推导跨语言单词簇的方法，可用于多语言解析器；2）将目标语言的词汇信息转移到源语言树库；3）将这些步骤与 Rasooli 和 Collins（2015）的密度驱动注释投影方法相结合。该方法在多个语言中实现了针对先前工作的最新进展，在只使用比利时圣经作为翻译数据的情况下，与以前的工作相比，Europarl 语料库的结果表现出额外的提升。最后，作者在通用依赖语料库的 38 个数据集上进行了结果展示。