This study introduces a novel multimodal corpus for expressive task-based spoken language and dialogue, focused on language use under frustration and surprise, elicited from three tasks motivated by prior research and collected in an IRB-approved experiment. The resource is unique both because these are understudied affect states for emotion modeling in language, and also because it provides both individual and dyadic multimodally grounded language. The study includes a detailed analysis of annotations and performance results for multimodal emotion inference in language use.

Motivated by prior literature, we provide a proof of concept simulation study for an understudied interactive machine learning method, machine teaching (MT), for the text-based emotion prediction task. We compare this method experimentally against a more well-studied technique, active learning (AL). Results show the strengths of both approaches over more resource-intensive offline supervised learning. Additionally, applying AL and MT to fine-tune a pre-trained model offers further efficiency gain. We end by recommending research directions which aim to empower users in the learning process.