Open-Domain Question Answering with Pre-Constructed Question Spaces
Jinfeng Xiao, Lidan Wang, Franck Dernoncourt, Trung Bui, Tong Sun,, Jiawei Han

TL;DR
This paper introduces a novel open-domain question answering method that pre-constructs question spaces offline and combines retrieval strategies, achieving higher accuracy than existing approaches.
Contribution
The paper presents a new reader-retriever algorithm that pre-constructs question-answer pairs offline and integrates multiple retrieval methods for improved accuracy.
Findings
Achieves superior accuracy on real-world datasets.
Effectively combines offline pre-constructed question spaces with online retrieval.
Addresses bottlenecks in existing open-domain QA systems.
Abstract
Open-domain question answering aims at solving the task of locating the answers to user-generated questions in massive collections of documents. There are two families of solutions available: retriever-readers, and knowledge-graph-based approaches. A retriever-reader usually first uses information retrieval methods like TF-IDF to locate some documents or paragraphs that are likely to be relevant to the question, and then feeds the retrieved text to a neural network reader to extract the answer. Alternatively, knowledge graphs can be constructed from the corpus and be queried against to answer user questions. We propose a novel algorithm with a reader-retriever structure that differs from both families. Our reader-retriever first uses an offline reader to read the corpus and generate collections of all answerable questions associated with their answers, and then uses an online retriever…
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Taxonomy
TopicsTopic Modeling · Natural Language Processing Techniques · Advanced Graph Neural Networks
