Characterizing Bias in Word Embeddings Towards Analyzing Gender Associations in Philippine Texts

The steady increase in computational gender bias research has been mostly done on languages for which reliable NLP packages are readily available—such as English, Chinese, and Spanish. This study expands on this area of research by using word embedding bias analysis methods in the Philippine context. To this end, Philippine media textual corpora consisting of 380 million English words and 921 million Filipino words were compiled and used to train FastText embeddings. These embeddings were then subjected to validation and to the Word Embedding Association Test (WEAT) to characterize bias in the embeddings and in the texts they were trained in. Results show that Filipino texts are associated with the heterosexual male by default, but strongest biases relate to the female and the non-heterosexual. Meanwhile, media texts written in English generally have more balanced gender associations compared to texts written in Filipino. Furthermore, the Filipino corpus links action more to the male and objects and social roles to the female. On the other hand, implicitly gendered words in English texts are mostly nouns. These results contribute to demonstrations of how WEAT can be applied in low-resource languages, such as Filipino.