Passage re-ranking is the task of scoring and re-ranking a collection of retrieved documents based on an input query.
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One technique to improve the retrieval effectiveness of a search engine is to expand documents with terms that are related or representative of the documents' content. From the perspective of a question answering system, this might comprise questions the document can potentially answer.
Ranked #1 on Passage Re-Ranking on MS MARCO
Recently, neural models pretrained on a language modeling task, such as ELMo (Peters et al., 2017), OpenAI GPT (Radford et al., 2018), and BERT (Devlin et al., 2018), have achieved impressive results on various natural language processing tasks such as question-answering and natural language inference.
Ranked #2 on Passage Re-Ranking on MS MARCO (using extra training data)