Efficient open-domain question-answering on Vespa.ai

Vespa.ai has been utilized to reproduce the state-of-the-art baseline for retrieval-based open-domain question-answering systems, offering a single, scalable, and production-ready platform. This blog post outlines how Vespa.ai consolidates various components essential for such systems, including BM25 text search, vector similarity search, and the integration of BERT-based models for encoding and extracting answers. The Efficient Open-Domain Question Answering challenge at NeurIPS 2020, which aims to advance question-answering systems, is discussed as a benchmark target. Vespa.ai's capabilities, such as hybrid retrieval using dense and sparse vectors, support for TensorFlow and PyTorch models, and a simplified deployment path without needing multiple subsystems, demonstrate its efficiency. The post details how Vespa uses the Natural Questions benchmark to evaluate its system's performance, achieving results in line with the Dense Passage Retrieval (DPR) paper, and outlines plans for future posts focusing on hybrid models and system latency improvements.