The Natural Questions corpus is a question answering dataset containing 307,373 training examples, 7,830 development examples, and 7,842 test examples. Each example is comprised of a google.com query and a corresponding Wikipedia page. Each Wikipedia page has a passage (or long answer) annotated on the page that answers the question and one or more short spans from the annotated passage containing the actual answer. The long and the short answer annotations can however be empty. If they are both empty, then there is no answer on the page at all. If the long answer annotation is non-empty, but the short answer annotation is empty, then the annotated passage answers the question but no explicit short answer could be found. Finally 1% of the documents have a passage annotated with a short answer that is “yes” or “no”, instead of a list of short spans.
705 PAPERS • 8 BENCHMARKS
The MS MARCO (Microsoft MAchine Reading Comprehension) is a collection of datasets focused on deep learning in search. The first dataset was a question answering dataset featuring 100,000 real Bing questions and a human generated answer. Over time the collection was extended with a 1,000,000 question dataset, a natural language generation dataset, a passage ranking dataset, keyphrase extraction dataset, crawling dataset, and a conversational search.
626 PAPERS • 7 BENCHMARKS
BEIR (Benchmarking IR) is an heterogeneous benchmark containing different information retrieval (IR) tasks. Through BEIR, it is possible to systematically study the zero-shot generalization capabilities of multiple neural retrieval approaches.
115 PAPERS • 20 BENCHMARKS
EntityQuestions is a dataset of simple, entity-rich questions based on facts from Wikidata (e.g., "Where was Arve Furset born? ").
19 PAPERS • 1 BENCHMARK
QAMPARI is an ODQA benchmark, where question answers are lists of entities, spread across many paragraphs. It was created by (a) generating questions with multiple answers from Wikipedia's knowledge graph and tables, (b) automatically pairing answers with supporting evidence in Wikipedia paragraphs, and (c) manually paraphrasing questions and validating each answer.
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CoreSearch is a dataset for Cross-Document Event Coreference Search. It consists of two separate passage collections: (1) a collection of passages containing manually annotated coreferring event mention, and (2) an annotated collection of destructor passages.
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GermanDPR is a dataset for passage retrieval in German. GermanDPR comprises 8,245 question/answer pairs in the training set, 1,030 pairs in the development set, and 1,025 pairs in the test set. For each pair, there are one positive context and three hard negative contexts.
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