# SexWEs: Domain-Aware Word Embeddings via Cross-lingual Semantic Specialisation for Chinese Sexism Detection in Social Media

arXiv: https://arxiv.org/abs/2211.08447 · HTML: https://mlsanity.com/paper/2211.08447/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 62 references that are in the map.
- First posted on arXiv 15 Nov 2022.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Convolutional Neural Networks for Sentence Classification](https://mlsanity.com/paper/1408.5882/) (2014)
- [Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks](https://mlsanity.com/paper/1703.10593/) (2017)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Revisiting Pre-Trained Models for Chinese Natural Language Processing](https://mlsanity.com/paper/2004.13922/) (2020)
- [Deep Learning for Hate Speech Detection in Tweets](https://mlsanity.com/paper/1706.00188/) (2017)
- [SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation](https://mlsanity.com/paper/1408.3456/) (2014)
- [Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion](https://mlsanity.com/paper/1804.07745/) (2018)
- [Cultural Shift or Linguistic Drift? Comparing Two Computational Measures of Semantic Change](https://mlsanity.com/paper/1606.02821/) (2016)
- [How to (Properly) Evaluate Cross-Lingual Word Embeddings: On Strong Baselines, Comparative Analyses, and Some Misconceptions](https://mlsanity.com/paper/1902.00508/) (2019)
- ["Call me sexist, but...": Revisiting Sexism Detection Using Psychological Scales and Adversarial Samples](https://mlsanity.com/paper/2004.12764/) (2020)
- [SWSR: A Chinese Dataset and Lexicon for Online Sexism Detection](https://mlsanity.com/paper/2108.03070/) (2021)
- [Semantic Specialisation of Distributional Word Vector Spaces using Monolingual and Cross-Lingual Constraints](https://mlsanity.com/paper/1706.00374/) (2017)
- [Multi-label Categorization of Accounts of Sexism using a Neural Framework](https://mlsanity.com/paper/1910.04602/) (2019)
- [Detecting White Supremacist Hate Speech using Domain Specific Word Embedding with Deep Learning and BERT](https://mlsanity.com/paper/2010.00357/) (2020)
- [Hate Speech Detection from Code-mixed Hindi-English Tweets Using Deep Learning Models](https://mlsanity.com/paper/1811.05145/) (2018)
- [Adversarial Propagation and Zero-Shot Cross-Lingual Transfer of Word Vector Specialization](https://mlsanity.com/paper/1809.04163/) (2018)
- [Learning Chinese Word Representations From Glyphs Of Characters](https://mlsanity.com/paper/1708.04755/) (2017)
- [Multi-Adversarial Learning for Cross-Lingual Word Embeddings](https://mlsanity.com/paper/2010.08432/) (2020)
- [Post-Specialisation: Retrofitting Vectors of Words Unseen in Lexical Resources](https://mlsanity.com/paper/1805.03228/) (2018)
- [VCWE: Visual Character-Enhanced Word Embeddings](https://mlsanity.com/paper/1902.08795/) (2019)
- [Cross-lingual hate speech detection based on multilingual domain-specific word embeddings](https://mlsanity.com/paper/2104.14728/) (2021)
- [RetroGAN: A Cyclic Post-Specialization System for Improving Out-of-Knowledge and Rare Word Representations](https://mlsanity.com/paper/2108.12941/) (2021)
- [Cross-Lingual Induction and Transfer of Verb Classes Based on Word Vector Space Specialisation](https://mlsanity.com/paper/1707.06945/) (2017)
- [Addressing the Challenges of Cross-Lingual Hate Speech Detection](https://mlsanity.com/paper/2201.05922/) (2022)

## Built on by (most cited first, 2 of 2)

- [DeMod: A Holistic Tool with Explainable Detection and Personalized Modification for Toxicity Censorship](https://mlsanity.com/paper/2411.01844/) (2024)
- [Cross-lingual Offensive Language Detection: A Systematic Review of Datasets, Transfer Approaches and Challenges](https://mlsanity.com/paper/2401.09244/) (2024)

## Abstract (arXiv)

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.