  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). For
both tasks we used this architecture and achieved 4th place out of 69
participants with an F1 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).
