Adaptive Contrastive Learning on Multimodal Transformer for Review Helpfulness Predictions

Understand

Modern Review Helpfulness Prediction systems are dependent upon multiple modalities, typically texts and images.

• Unfortunately, those contemporary approaches pay scarce attention to polish representations of cross-modal relations and tend to suffer from inferior optimization.
• This might cause harm to model's predictions in numerous cases.
• To overcome the aforementioned issues, we propose Multimodal Contrastive Learning for Multimodal Review Helpfulness Prediction (MRHP) problem, concentrating on mutual information between input modalities to explicitly elaborate cross-modal relations.