MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering
In this paper, we introduce MIX : a multi-task deep learning approach to solve Open-Domain Question Answering. First, we design our system as a multi-stage pipeline made of 3 building blocks : a BM25-based Retriever, to reduce the search space; RoBERTa based Scorer and Extractor, to rank retrieved paragraphs and extract relevant spans of text respectively. Eventually, we further improve computational efficiency of our system to deal with the scalability challenge : thanks to multi-task learning, we parallelize the close tasks solved by the Scorer and the Extractor. Our system is on par with state-of-the-art performances on the squad-open benchmark while being simpler conceptually.
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