Aspects of the disclosure are directed to a multimodal processing system for processing both structured and un-structured data. Real-world data is not always consistent in form or content. The multimodal processing system includes model that can be trained to account for this characteristic of real-world data, by selectively masking data of different modalities during pretraining to learn outputs that are the same or comparable between the masked and un-masked inputs. The model is trained according to modality-specific masking objectives computed for each modality of data and joint modality similarity-based masking objectives for a joint representation of the data across all modalities. The system provides consistent and accurate input, even when input data may have substantial portions of data from different modalities missing. Cross-modal relationships in data are reinforced by the model as different portions of data are masked, contributing to an overall increase in model accuracy versus other approaches.