Recent strides in the healthcare domain, have resulted in vast quantities of streaming data available for use for building intelligent knowledge-based applications. However, the challenges introduced to the huge volume, velocity of generation, variety and variability of this medical data have to be adequately addressed. In this paper, we describe the model and results for our submission at MEDIQA 2021 Question Summarization shared task. In order to improve the performance of summarization of consumer health questions, our method explores the use of transfer learning to utilize the knowledge of NLP transformers like BART, T5 and PEGASUS. The proposed models utilize the knowledge of pre-trained NLP transformers to achieve improved results when compared to conventional deep learning models such as LSTM, RNN etc. Our team SB_NITK ranked 12th among the total 22 submissions in the official final rankings. Our BART based model achieved a ROUGE-2 F1 score of 0.139.

Retrieving relevant answers from heterogeneous data formats, for given for questions, is a challenging problem. The process of pinpointing relevant information suitable to answer a question is further compounded in large document collections containing documents of substantial length. This paper presents the models designed as part of our submission to the DialDoc21 Shared Task (Document-grounded Dialogue and Conversational Question Answering) for span prediction in question answering. The proposed models leverage the superior predictive power of pretrained transformer models like RoBERTa, ALBERT and ELECTRA, to identify the most relevant information in an associated passage for the next agent turn. To further enhance the performance, the models were fine-tuned on different span selection based question answering datasets like SQuAD2.0 and Natural Questions (NQ) corpus. We also explored ensemble techniques for combining multiple models to achieve enhanced performance for the task. Our team SB_NITK ranked 6th on the leaderboard for the Knowledge Identification task, and our best ensemble model achieved an Exact score of 58.58 and an F1 score of 73.39.