The COVID-19 global pandemic has resulted in international efforts to
understand, track, and mitigate the disease, yielding a significant corpus of
COVID-19 and SARS-CoV-2-related publications across scientific disciplines. As
of May 2020, 128,000 coronavirus-related publications have been collected
through the COVID-19 Open Research Dataset Challenge. Here we present
CO-Search, a retriever-ranker semantic search engine designed to handle complex
queries over the COVID-19 literature, potentially aiding overburdened health
workers in finding scientific answers during a time of crisis. The retriever is
built from a Siamese-BERT encoder that is linearly composed with a TF-IDF
vectorizer, and reciprocal-rank fused with a BM25 vectorizer. The ranker is
composed of a multi-hop question-answering module, that together with a
multi-paragraph abstractive summarizer adjust retriever scores. To account for
the domain-specific and relatively limited dataset, we generate a bipartite
graph of document paragraphs and citations, creating 1.3 million (citation
title, paragraph) tuples for training the encoder. We evaluate our system on
the data of the TREC-COVID information retrieval challenge. CO-Search obtains
top performance on the datasets of the first and second rounds, across several
key metrics: normalized discounted cumulative gain, precision, mean average
precision, and binary preference.

本文介绍了 CO-Search，这是一款检索 - 排序语义搜索引擎，设计用于处理 COVID-19 相关文献，包括使用 Siamese-BERT 编码器、BM25 矢量化器和多跳问答模块等，以高效地帮助寻找科学答案，并在多项指标上获得了最佳表现。