Over the last years, word and sentence embeddings have established as text
preprocessing for all kinds of NLP tasks and improved performances in these
tasks significantly. Unfortunately, it has also been shown that these
embeddings inherit various kinds of biases from the training data and thereby
pass on biases present in society to NLP solutions. Many papers attempted to
quantify bias in word or sentence embeddings to evaluate debiasing methods or
compare different embedding models, often with cosine-based scores. However,
some works have raised doubts about these scores showing that even though they
report low biases, biases persist and can be shown with other tests. In fact,
there is a great variety of bias scores or tests proposed in the literature
without any consensus on the optimal solutions. We lack works that study the
behavior of bias scores and elaborate their advantages and disadvantages. In
this work, we will explore different cosine-based bias scores. We provide a
bias definition based on the ideas from the literature and derive novel
requirements for bias scores. Furthermore, we thoroughly investigate the
existing cosine-based scores and their limitations in order to show why these
scores fail to report biases in some situations. Finally, we propose a new bias
score, SAME, to address the shortcomings of existing bias scores and show
empirically that SAME is better suited to quantify biases in word embeddings.

本文研究了利用余弦相似度对词嵌入进行编码的偏见评分，提出了一种新的偏见评分方法 SAME，并用实验证明其比现有的评分方法更能准确评估词嵌入中的偏见。

SAME 分数：基于余弦的词向量偏差分数的改进

The SAME score: Improved cosine based bias score for word embeddings

In this paper we present the first work on the automated scoring of
mindreading ability in middle childhood and early adolescence. We create
MIND-CA, a new corpus of 11,311 question-answer pairs in English from 1,066
children aged 7 to 14. We perform machine learning experiments and carry out
extensive quantitative and qualitative evaluation. We obtain promising results,
demonstrating the applicability of state-of-the-art NLP solutions to a new
domain and task.

本文的第一项工作是自动评分中儿童和早期青春期的心智阅读能力。通过创建 MIND-CA 的新语料库，作者对 1,066 名 7 至 14 岁儿童创建了 11,311 个问题 - 回答对。作者进行了机器学习实验并进行了广泛的定量和定性评估。结果很有前途，证明了最先进的 NLP 解决方案对于新的领域和任务具有适用性。

童年和早期青春期心理阅读能力的自动化评分

"What is on your mind?" Automated Scoring of Mindreading in Childhood  and Early Adolescence

Fake news detection is a critical yet challenging problem in Natural Language
Processing (NLP). The rapid rise of social networking platforms has not only
yielded a vast increase in information accessibility but has also accelerated
the spread of fake news. Thus, the effect of fake news has been growing,
sometimes extending to the offline world and threatening public safety. Given
the massive amount of Web content, automatic fake news detection is a practical
NLP problem useful to all online content providers, in order to reduce the
human time and effort to detect and prevent the spread of fake news. In this
paper, we describe the challenges involved in fake news detection and also
describe related tasks. We systematically review and compare the task
formulations, datasets and NLP solutions that have been developed for this
task, and also discuss the potentials and limitations of them. Based on our
insights, we outline promising research directions, including more
fine-grained, detailed, fair, and practical detection models. We also highlight
the difference between fake news detection and other related tasks, and the
importance of NLP solutions for fake news detection.

本文介绍了如何应用自然语言处理技术解决社交网络上的虚假新闻检测问题，并着重描述了该问题的挑战以及相关任务的形式、数据集和解决方案，并讨论了其潜力和局限性，并提出了更精细、详细、公平和实用的检测模型等研究方向。