Information Retrieval in Natural Language Processing

Modern Question Answering (QA) systems consist of two components: readers and retrievers. Retrievers reduce the passage search space for answer extraction and limit the overall accuracy of QA methods. Conventional retrievers consume large amounts of resources, reducing their viability to large corporations or well funded institutions. In this paper, we outline a methodology for building information retrieval systems on a limited budget and perform feature enhancement using transfer learning. Through several ablation studies we demonstrate that existing DPR approaches are very sensitive to small changes in the problem domain, and introduce an approach to potentially improve generalizability which outperforms the existing DPR framework under one ablation. We also highlight a potential data quality issue from a well-cited paper, which may call into question published accuracy metrics and warrant additional review.