MIX : a Multi-task Learning Approach to Solve Open-Domain Question Answering
This work provides a computationally efficient and conceptually simpler solution for open-domain question answering, which is beneficial for researchers and practitioners working with large datasets.
This paper introduces MIX, a multi-task learning approach for open-domain question answering that combines a BM25 retriever, a RoBERTa scorer, and an extractor. The system achieves state-of-the-art performance on the squad-open benchmark while improving computational efficiency through parallelization of the scorer and extractor tasks.
This paper introduces MIX, a multi-task deep learning approach to solve open-ended question-answering. First, we design our system as a multi-stage pipeline of 3 building blocks: a BM25-based Retriever to reduce the search space, a RoBERTa-based Scorer, and an Extractor to rank retrieved paragraphs and extract relevant text spans, respectively. Eventually, we further improve the 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.