DAM-Net: Robust QA System with Data Augmentation and Multitask Learning

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If the machine can comprehend a passage and answer questions based on the context, how to upgrade a QA system to generalize to unseen domains outside the training data? In this project, we propose DAM-Net, a robust QA model that can achieve strong performance even on test examples drawn beyond their training distributions. Specifically, we perform data augmentation on our training data, expand training with the auxiliary task (i.e. fill-in-the-blank), and utilize multi-domain training with additional fine-tuning. DAM-Net has shown strong performance on the robust QA benchmark and sometimes it even outperforms humans in terms of the comprehensiveness and accuracy of the answers!