QUASAR (QUestion Answering by Search And Reading)
Introduced by Dhingra et al. in Quasar: Datasets for Question Answering by Search and Reading
The Question Answering by Search And Reading (QUASAR) is a large-scale dataset consisting of QUASAR-S and QUASAR-T. Each of these datasets is built to focus on evaluating systems devised to understand a natural language query, a large corpus of texts and to extract an answer to the question from the corpus. Specifically, QUASAR-S comprises 37,012 fill-in-the-gaps questions that are collected from the popular website Stack Overflow using entity tags. The QUASAR-T dataset contains 43,012 open-domain questions collected from various internet sources. The candidate documents for each question in this dataset are retrieved from an Apache Lucene based search engine built on top of the ClueWeb09 dataset.
Source: MRNN: A Multi-Resolution Neural Network with Duplex Attention for Document Retrieval in the Context of Question AnsweringBenchmarks
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