Ranking Paragraphs for Improving Answer Recall in Open-Domain Question Answering

Understand

Recently, open-domain question answering (QA) has been combined with machine comprehension models to find answers in a large knowledge source.

• As open-domain QA requires retrieving relevant documents from text corpora to answer questions, its performance largely depends on the performance of document retrievers.
• However, since traditional information retrieval systems are not effective in obtaining documents with a high probability of containing answers, they lower the performance of QA systems.
• Simply extracting more documents increases the number of irrelevant documents, which also degrades the performance of QA systems.