The goal of sexism detection is to mitigate negative online content targeting
certain gender groups of people. However, the limited availability of labeled
sexism-related datasets makes it problematic to identify online sexism for
low-resource languages. In this paper, we address the task of automatic sexism
detection in social media for one low-resource language -- Chinese. Rather than
collecting new sexism data or building cross-lingual transfer learning models,
we develop a cross-lingual domain-aware semantic specialisation system in order
to make the most of existing data. Semantic specialisation is a technique for
retrofitting pre-trained distributional word vectors by integrating external
linguistic knowledge (such as lexico-semantic relations) into the specialised
feature space. To do this, we leverage semantic resources for sexism from a
high-resource language (English) to specialise pre-trained word vectors in the
target language (Chinese) to inject domain knowledge. We demonstrate the
benefit of our sexist word embeddings (SexWEs) specialised by our framework via
intrinsic evaluation of word similarity and extrinsic evaluation of sexism
detection. Compared with other specialisation approaches and Chinese baseline
word vectors, our SexWEs shows an average score improvement of 0.033 and 0.064
in both intrinsic and extrinsic evaluations, respectively. The ablative results
and visualisation of SexWEs also prove the effectiveness of our framework on
retrofitting word vectors in low-resource languages. Our code and
sexism-related word vectors will be publicly available.

本文提出了一种跨语言领域感知语义专业化系统，利用高资源语言（英语）的语义资源来专业化低资源语言（中文）的已预训练词向量，从而实现社交媒体中低资源语言性别歧视检测。通过内在评估和外在评估，证明了该系统显著提高了歧视识别的词性和表现。