Uncover Sexual Harassment Patterns from Personal Stories by Joint Key Element Extraction and Categorization
Table 4: Harassment form classification accuracy of models. * Reported by Karlekar and Bansal Karlekar and Bansal (2018)
['Models', 'commenting', 'ogling', 'groping']
Classification on Harassment Forms: Joint learning models achieved higher accuracy. In some harassment stories, the whole text or a span of the text consists of trigger words of multiple forms, such as “stare, whistles, start to sing, commenting”. The supervised attention mechanism will force the model to look at all such words rather than just the one related to the harassment form for classification and hence it can introduce noise. This can explain why J-SACNN got lower accuracy in two of the harassment form classifications, compared to J-ACNN. In addition, J-CNN model did best in “ogling” classification.