End-to-End Retrieval in Continuous Space

Understand

Most text-based information retrieval (IR) systems index objects by words or phrases.

• These discrete systems have been augmented by models that use embeddings to measure similarity in continuous space.
• But continuous-space models are typically used just to re-rank the top candidates.
• We consider the problem of end-to-end continuous retrieval, where standard approximate nearest neighbor (ANN) search replaces the usual discrete inverted index, and rely entirely on distances between learned embeddings.