Amobee at SemEval-2019 Tasks 5 and 6: Multiple Choice CNN Over Contextual Embedding
Alon Rozental, Dadi Biton · 2019
This article describes Amobee's participation in "HatEval: Multilingual detection of hate speech against immigrants and women in Twitter" (task 5) and "OffensEval: Identifying and Categorizing Offensive Language in Social Media" (task 6).The goal of task 5 was to detect hate speech targeted to women and immigrants.The goal of task 6 was to identify and categorized offensive language in social media, and identify offense target.We present a novel type of convolutional neural network called "Multiple Choice CNN" (MC-CNN) that we used over our newly developed contextual embedding, Rozental et al. (2019) 1 .For both tasks we used this architecture and achieved 4th place out of 69 participants with an F 1 score of 0.53 in task 5, in task 6 achieved 2nd place (out of 75) in Sub-task B -automatic categorization of offense types (our model reached places 18/2/7 out of 103/75/65 for sub-tasks A, B and C respectively in task 6).