Open-domain question answering aims at solving the task of locating the answers to user-generated questions in large collections of documents. There are two families of solutions to this challenge. One family of algorithms, namely retriever-readers, first retrieves some pieces of text that are probably relevant to the question, and then feeds the retrieved text to a neural network to get the answer. Another line of work first constructs some knowledge graphs from the corpus , and queries the graph for the answer . We propose a novel algorithm with a reader-retriever structure that differs from both families. Our algorithm first reads off-line the corpus to generate collections of all answerable questions associated with their answers, and then queries the pre-constructed question spaces online to find answers that are most likely to be asked in the given way. The final answer returned to the user is decided with an accept-or-reject mechanism that combines multiple candidate answers by comparing the level of agreement between the retriever-reader and reader-retriever results . We claim that our algorithm solves some bottlenecks in existing work, and demonstrate that it achieves superior accuracy on a public dataset .
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 to respond to user queries by searching the pre-constructed question spaces for answers that are most likely to be asked in the given way. We further combine retriever-reader and reader-retriever results into one single answer by examining the consistency between the two components . We claim that our algorithm solves some bottlenecks in existing work, and demonstrate that it achieves superior accuracy on real-world datasets .
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