Word embedding methods (WEMs) are extensively used for representing text
data. The dimensionality of these embeddings varies across various tasks and
implementations. The effect of dimensionality change on the accuracy of the
downstream task is a well-explored question. However, how the dimensionality
change affects the bias of word embeddings needs to be investigated. Using the
English Wikipedia corpus, we study this effect for two static (Word2Vec and
fastText) and two context-sensitive (ElMo and BERT) WEMs. We have two
observations. First, there is a significant variation in the bias of word
embeddings with the dimensionality change. Second, there is no uniformity in
how the dimensionality change affects the bias of word embeddings. These
factors should be considered while selecting the dimensionality of word
embeddings.

使用英文维基百科语料库，研究了维度变化对词嵌入偏差的影响，结果显示词嵌入的偏差会随着维度变化而显著变化，且对不同的词嵌入方法影响各异，因此在选择词嵌入维度时应考虑这些因素。

维度变化对词嵌入偏差的影响

Effect of dimensionality change on the bias of word embeddings

Human activities can be seen as sequences of events, which are crucial to
understanding societies. Disproportional event distribution for different
demographic groups can manifest and amplify social stereotypes, and potentially
jeopardize the ability of members in some groups to pursue certain goals. In
this paper, we present the first event-centric study of gender biases in a
Wikipedia corpus. To facilitate the study, we curate a corpus of career and
personal life descriptions with demographic information consisting of 7,854
fragments from 10,412 celebrities. Then we detect events with a
state-of-the-art event detection model, calibrate the results using
strategically generated templates, and extract events that have asymmetric
associations with genders. Our study discovers that the Wikipedia pages tend to
intermingle personal life events with professional events for females but not
for males, which calls for the awareness of the Wikipedia community to
formalize guidelines and train the editors to mind the implicit biases that
contributors carry. Our work also lays the foundation for future works on
quantifying and discovering event biases at the corpus level.

本文首次对维基百科语料库中职业和个人生活描述中的性别偏见进行了事件中心研究，发现维基百科页面倾向于在女性但不在男性的个人生活事件中夹杂职业事件，应提高维基社区意识以制定指南和培训编辑。

维基百科上的性别偏见：男性当选，女性成婚

Men Are Elected, Women Are Married: Events Gender Bias on Wikipedia

Models for question answering, dialogue agents, and summarization often
interpret the meaning of a sentence in a rich context and use that meaning in a
new context. Taking excerpts of text can be problematic, as key pieces may not
be explicit in a local window. We isolate and define the problem of sentence
decontextualization: taking a sentence together with its context and rewriting
it to be interpretable out of context, while preserving its meaning. We
describe an annotation procedure, collect data on the Wikipedia corpus, and use
the data to train models to automatically decontextualize sentences. We present
preliminary studies that show the value of sentence decontextualization in a
user facing task, and as preprocessing for systems that perform document
understanding. We argue that decontextualization is an important subtask in
many downstream applications, and that the definitions and resources provided
can benefit tasks that operate on sentences that occur in a richer context.

本文研究了句子脱离语境的问题及其在自然语言处理模型中的重要性，并通过 Wikipedia 语料库训练了自动化解决方案。研究表明句子脱离语境对用户工作任务和文档理解预处理都具有重要价值。