Query Context Expansion for Open-Domain Question Answering

Abstract

Humans are accustomed to autonomously associating prior knowledge with the text in a query when answering questions. However, for machines lacking cognition and common sense, a query is merely a combination of some words. Although we can enrich the semantic information of the given query through language representation or query expansion (QE) , the information contained in the query is still insufficient. In this paper, we propose an effective passage retrieval method named query context expansion-based retrieval (QCER) for open-domain question answering (OpenQA) . QCER associates a query with domain information by adding contextual association information based on the pseudo-relevance feedback (PRF) . QCER uses a dense reader to select top-n expansion terms for QE. We implement QCER by appending reader predictions, theoretically present in candidate passages, as contextual information to the initial query to form the new query. QCER with sparse representations (BM25) can improve retrieval efficiency and accelerate query convergence so that the reader can find the desired answer using fewer relevant passages, e.g., 10 passages, as soon as possible. Moreover, QCER can be easily combined with dense passage retrieval (DPR) to achieve even better performance, as sparse and dense representations are often complementary. Remarkably, we demonstrate that QCER achieves state-of-the-art performance in three tasks, passage retrieval, passage reading, and passage reranking, on the Natural Questions (NQ) and TriviaQA (Trivia) datasets under an extractive QA setup.