Multimodal Tweet Understanding
A Tweet contains information that goes beyond 280 characters. For instance the text in a Tweet provides information enriched with emojis, #hashtags, and @mentions. However, a Tweet contains other modalities such as images, likes, and trends representing each one a piece of a puzzle. Using Machine Learning to learn from these signals allows us a more general understanding of Tweets. In this talk, I present how to learn multimodal representations from Tweets using Transformers and neural architectures, these methods are studied for understanding memes or modeling toxicity.
Omar is a NLP Researcher at Twitter Cortex, working on state-of-the-art research on enabling natural conversations between humans and devices. Implementing meta-learning and memory augmented recurrent neural networks to learn with small data, as we humans do, and to deal with catastrophic forgetting.
