A Mutli-Task Mutlimodal Framework for Tweet Classification Based on CNN (Grand Challenge)

The paper describes the system description and design for our solution submitted for the MeToo BigMM Grand Challenge (BMGC). The challenge involves building a multimodal framework for predicting the linguistic aspect of tweets such as relevance, stance, hate speech, sarcasm and dialogue acts. As a whole, there are 10 different categories that are to be predicted. For the challenge we try several different approaches based on Bidirectional LSTM with attention and Convolutional Neural Networks. We describe in detail our insights behind the various steps involved from the preprocessing to defining the model architecture. We further provide a detailed analysis of the results obtained from the challenge. Overall our team ranked 4th on the final leaderboard of the challenge.