Detecting hate speech in the workplace is a unique classification task, as
the underlying social context implies a subtler version of conventional hate
speech. Applications regarding a state-of the-art workplace sexism detection
model include aids for Human Resources departments, AI chatbots and sentiment
analysis. Most existing hate speech detection methods, although robust and
accurate, focus on hate speech found on social media, specifically Twitter. The
context of social media is much more anonymous than the workplace, therefore it
tends to lend itself to more aggressive and "hostile" versions of sexism.
Therefore, datasets with large amounts of "hostile" sexism have a slightly
easier detection task since "hostile" sexist statements can hinge on a couple
words that, regardless of context, tip the model off that a statement is
sexist. In this paper we present a dataset of sexist statements that are more
likely to be said in the workplace as well as a deep learning model that can
achieve state-of-the art results. Previous research has created
state-of-the-art models to distinguish "hostile" and "benevolent" sexism based
simply on aggregated Twitter data. Our deep learning methods, initialized with
GloVe or random word embeddings, use LSTMs with attention mechanisms to
outperform those models on a more diverse, filtered dataset that is more
targeted towards workplace sexism, leading to an F1 score of 0.88.

本文提出一种基于深度学习和注意力机制的性别歧视检测模型，该模型使用 GloVe 或随机词嵌入，并应用于一组更加有针对性的和多样化的数据集，以获得 0.88 的 F1 分数，以区分更加微妙和难以检测的工作场所性别歧视言论。