# Amobee at SemEval-2019 Tasks 5 and 6: Multiple Choice CNN Over Contextual Embedding

## TL;DR

This paper presents Amobee's participation in SemEval-2019 tasks on hate speech and offensive language detection, utilizing a novel Multiple Choice CNN architecture over contextual embeddings, achieving top-tier results.

## Contribution

Introduction of the Multiple Choice CNN architecture combined with contextual embeddings for improved social media hate speech and offensive language detection.

## Key findings

- Achieved 4th place in hate speech detection with F1 score 0.53
- Secured 2nd place in offensive language categorization
- Model outperformed many competitors in multiple sub-tasks

## Abstract

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).