Modeling Text-visual Mutual Dependency for Multi-modal Dialog Generation
This addresses multi-modal dialog modeling for applications like virtual assistants, though it appears incremental as it builds on existing frameworks with a specific enhancement.
The paper tackles multi-modal dialog generation by modeling mutual dependency between text and visual features, where the model predicts both the next dialog utterance and the visual context, resulting in significant performance boosts over vanilla models.
Multi-modal dialog modeling is of growing interest. In this work, we propose frameworks to resolve a specific case of multi-modal dialog generation that better mimics multi-modal dialog generation in the real world, where each dialog turn is associated with the visual context in which it takes place. Specifically, we propose to model the mutual dependency between text-visual features, where the model not only needs to learn the probability of generating the next dialog utterance given preceding dialog utterances and visual contexts, but also the probability of predicting the visual features in which a dialog utterance takes place, leading the generated dialog utterance specific to the visual context. We observe significant performance boosts over vanilla models when the mutual dependency between text and visual features is modeled. Code is available at https://github.com/ShannonAI/OpenViDial.