Answering Any-hop Open-domain Questions with Iterative Document Reranking
arXiv:2009.07465
Abstract
Existing approaches for open-domain question answering (QA) are typically designed for questions that require either single-hop or multi-hop reasoning, which make strong assumptions of the complexity of questions to be answered. Also, multi-step document retrieval often incurs higher number of relevant but non-supporting documents, which dampens the downstream noise-sensitive reader module for answer extraction. To address these challenges, we propose a unified QA framework to answer any-hop open-domain questions, which iteratively retrieves, reranks and filters documents, and adaptively determines when to stop the retrieval process. To improve the retrieval accuracy, we propose a graph-based reranking model that perform multi-document interaction as the core of our iterative reranking framework. Our method consistently achieves performance comparable to or better than the state-of-the-art on both single-hop and multi-hop open-domain QA datasets, including Natural Questions Open, SQuAD Open, and HotpotQA.
Accepted by SIGIR 2021
References in corpus (12)
- Inductive Representation Learning on Large Graphs
- HuggingFace's Transformers: State-of-the-art Natural Language Processing
- End-to-End Open-Domain Question Answering with BERTserini
- Know What You Don't Know: Unanswerable Questions for SQuAD
- Variational Reasoning for Question Answering with Knowledge Graph
- Evidence Aggregation for Answer Re-Ranking in Open-Domain Question Answering
- Knowledge Guided Text Retrieval and Reading for Open Domain Question Answering
- Modeling Semantics with Gated Graph Neural Networks for Knowledge Base Question Answering
- Differentiable Reasoning over a Virtual Knowledge Base
- Dynamically Fused Graph Network for Multi-hop Reasoning
- Multi-step Retriever-Reader Interaction for Scalable Open-domain Question Answering
- Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering