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DuReader: a Chinese Machine Reading Comprehension Dataset from Real-world Applications
In this paper, we introduce DuReader, a new large-scale, open-domain Chi...
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Clinical Reading Comprehension: A Thorough Analysis of the emrQA Dataset
Machine reading comprehension has made great progress in recent years ow...
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Adaptations of ROUGE and BLEU to Better Evaluate Machine Reading Comprehension Task
Current evaluation metrics to question answering based machine reading c...
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CliCR: A Dataset of Clinical Case Reports for Machine Reading Comprehension
We present a new dataset for machine comprehension in the medical domain...
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Embracing data abundance: BookTest Dataset for Reading Comprehension
There is a practically unlimited amount of natural language data availab...
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BIOMRC: A Dataset for Biomedical Machine Reading Comprehension
We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset. C...
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Templates of generic geographic information for answering where-questions
In everyday communication, where-questions are answered by place descrip...
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MS MARCO: A Human Generated MAchine Reading COmprehension Dataset
This paper presents our recent work on the design and development of a new, large scale dataset, which we name MS MARCO, for MAchine Reading COmprehension.This new dataset is aimed to overcome a number of well-known weaknesses of previous publicly available datasets for the same task of reading comprehension and question answering. In MS MARCO, all questions are sampled from real anonymized user queries. The context passages, from which answers in the dataset are derived, are extracted from real web documents using the most advanced version of the Bing search engine. The answers to the queries are human generated. Finally, a subset of these queries has multiple answers. We aim to release one million queries and the corresponding answers in the dataset, which, to the best of our knowledge, is the most comprehensive real-world dataset of its kind in both quantity and quality. We are currently releasing 100,000 queries with their corresponding answers to inspire work in reading comprehension and question answering along with gathering feedback from the research community.
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