  Open-domain question answering can be formulated as a phrase retrieval
problem, in which we can expect huge scalability and speed benefit but often
suffer from low accuracy due to the limitation of existing phrase
representation models. In this paper, we aim to improve the quality of each
phrase embedding by augmenting it with a contextualized sparse representation
(Sparc). Unlike previous sparse vectors that are term-frequency-based (e.g.,
tf-idf) or directly learned (only few thousand dimensions), we leverage
rectified self-attention to indirectly learn sparse vectors in n-gram
vocabulary space. By augmenting the previous phrase retrieval model (Seo et
al., 2019) with Sparc, we show 4%+ improvement in CuratedTREC and SQuAD-Open.
Our CuratedTREC score is even better than the best known retrieve & read model
with at least 45x faster inference speed.
