Amrita School of Engineering - CSE at SemEval-2019 Task 6: Manipulating Attention with Temporal Convolutional Neural Network for Offense Identification and Classification

Abstract

With the proliferation and ubiquity of smart gadgets and smart devices, across the world, data generated by them has been growing at exponential rates, in particular social media platforms like Facebook, Twitter and Instagram have been generating voluminous data on a daily basis. According to Twitter's usage statistics, about 500 million tweets are generated each day. While the tweets reflect the users' opinions on several events across the world, there are tweets which are offensive in nature that need to be tagged under the hateful conduct policy of Twitter. Offensive tweets have to be identified, captured and processed further, for a variety of reasons, which include i) identifying offensive tweets in order to prevent violent/abusive behaviour in Twitter (or any social media for that matter), ii) creating and maintaining a history of offensive tweets for individual users (would be helpful in creating meta-data for user profile), iii) inferring the sentiment of the users on particular event/issue/topic. We (CodaLab Team/User Name: murali sr) have employed neural network models which manipulate attention with Temporal Convolutional Neural Network for the three shared subtasks i) ATT-TCN (ATTention based Temporal Convolutional Neural Network) employed for shared sub-task A that yielded a best macro-F1 score of 0.46, ii) SAE-ATT-TCN(Self Attentive Embedding-ATTention based Temporal Convolutional Neural Network) employed for shared sub-task B and sub-task C that yielded best macro-F1 score of 0.61 and 0.51 respectively. Among the two variants ATT-TCN and SAE-ATT-TCN, the latter performed better.

Classification of Imbalanced Offensive Dataset – Sentence Generation for Minority Class with LSTM

“…Rozental et al [19] proposed a Multiple Choice Convolutional Neural Network (MC-CNN) which was fed into contextual embedding generated from Twitter and achieved a 0.7868 F-score. In [20] Temporal Convolutional Neural Network with an attention layer was applied and the authors received a 0.4682 Fscore. Also, they claimed that preserving as many even numbers of samples for each class as possible could increase classification accuracy when the dataset had a class imbalance ratio.…”