Pratik Ringshia

TL;DR: This work proposes a new challenge set for multimodal classification, focusing on detecting hate speech in multi-modal memes, constructed such that unimodal models struggle and only multimodAL models can succeed.

TL;DR: It is argued that Dynabench addresses a critical need in the community: contemporary models quickly achieve outstanding performance on benchmark tasks but nonetheless fail on simple challenge examples and falter in real-world scenarios.

TL;DR: The authors proposed a new challenge set for multimodal classification, focusing on detecting hate speech in multi-modal memes, where difficult examples are added to the dataset to make it hard to rely on unimodal signals.

TL;DR: The properties of continual learning, providing engaging content, and being well-behaved are discussed -- and how to measure success in providing them and their recommendations to the community are discussed.

TL;DR: This work introduces neural network based models to compositionally arrange locations, characters, and objects into a coherent whole and shows that the game environments created with this approach are cohesive, diverse, and preferred by human evaluators compared to other machine learning based world construction algorithms.