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Parallel deep learning architectures like fine-tuned BERT and MT-DNN, havequickly become the state of the art, bypassing previous deep and shallowlearning methods by a large margin. More recently, pre-trained models fromlarge related datasets have been able to perform well on many downstream tasksby just fine-tuning on domain-specific datasets . However, using powerfulmodels on non-trivial tasks, such as ranking and large document classification,still remains a challenge due to input size limitations of parallelarchitecture and extremely small datasets (insufficient for fine-tuning). Inthis work, we introduce an end-to-end system, trained in a multi-task setting,to filter and re-rank answers in the medical domain. We use task-specificpre-trained models as deep feature extractors. Our model achieves the highestSpearman's Rho and Mean Reciprocal Rank of 0.338 and 0.9622 respectively, onthe ACL-BioNLP workshop MediQA Question Answering shared-task.

Pentagon at MEDIQA 2019: Multi-task Learning for Filtering and Re-ranking Answers using Language Inference and Question Entailment