InterBERT: An Effective Multi-Modal Pretraining Approach via Vision-and-Language Interaction
Table 3: Results on the GLUE dev set. We evaluate the performance of the BERT-base model, a single-stream model, and InterBERT on 8 tasks of GLUE. The results show that InterBERT can rival the BERT-base model in the tasks of natural language understanding. We report F1 scores for QQP and MRPC, Spearman correlations for STS-B, and accuracy scores for the rest.
['Model', 'QNLI', 'CoLA', 'SST-2', 'STS-B', 'RTE', 'MNLI (m/mm)', 'QQP', 'MRPC', 'Avg.']
This indicates that InterBERT with the two-stream extraction module preserves the ability to model single-modal representations and it can adapt to single-modal downstream tasks without significant performance decrease.