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We introduce SPARTA, a novel neural retrieval method that shows great promisein performance, generalization, and interpretability for open-domain questionanswering. Unlike many neural ranking methods that use dense vector nearestneighbor search, SPARTA learns a sparse representation that can be efficientlyimplemented as an Inverted Index. The resulting representation enables scalableneural retrieval that does not require expensive approximate vector search andleads to better performance than its dense counterpart. We validated ourapproaches on 4 open-domain question answering (OpenQA) tasks and 11 retrievalquestion answering (ReQA) tasks. SPARTA achieves new state-of-the-art resultsacross a variety of open-domain question answering tasks in both English andChinese datasets, including open SQuAD, Natuarl Question, CMRC and etc.Analysis also confirms that the proposed method creates human interpretablerepresentation and allows flexible control over the trade-off betweenperformance and efficiency.

SPARTA: Efficient Open-Domain Question Answering via Sparse Transformer Matching Retrieval