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In information retrieval, an open-domain question answering system tries to return an answer in response to the user's question. The returned answer is in the form of short texts rather than a list of relevant documents. [13] The system finds answers by using a combination of techniques from computational linguistics, information retrieval, and ...
Another benchmark dataset is the GeoQuery dataset which contains questions about the geography of the U.S. paired with corresponding Prolog. [27] The Overnight dataset is used to test how well semantic parsers adapt across multiple domains; it contains natural language queries about 8 different domains paired with corresponding λ-DCS ...
Question-query pairs Question Answering 2018 [332] [333] Hartmann, Soru, and Marx et al. Vietnamese Question Answering Dataset (UIT-ViQuAD) A large collection of Vietnamese questions for evaluating MRC models. This dataset comprises over 23,000 human-generated question-answer pairs based on 5,109 passages of 174 Vietnamese articles from ...
The HAI researchers, who included a Stanford Law professor, created a dataset of 200 questions designed to mimic the kinds of questions a lawyer might ask a legal research copilot.
Large dataset of images for object classification. Images categorized and hand-sorted. 30,607 Images, Text Classification, object detection 2007 [29] [30] G. Griffin et al. COYO-700M Image–text-pair dataset 10 billion pairs of alt-text and image sources in HTML documents in CommonCrawl 746,972,269 Images, Text Classification, Image-Language ...
A question answering task is considered "open book" if the model's prompt includes text from which the expected answer can be derived (for example, the previous question could be adjoined with some text which includes the sentence "The Sharks have advanced to the Stanley Cup finals once, losing to the Pittsburgh Penguins in 2016." [125]).
Accurate legal information retrieval is important to provide access to the law to laymen and legal professionals. Its importance has increased because of the vast and quickly increasing amount of legal documents available through electronic means. [2] Legal information retrieval is a part of the growing field of legal informatics.
HYPO is a computer program, an expert system, that models reasoning with cases and hypotheticals in the legal domain. It is the first of its kind and the most sophisticated of the case-based legal reasoners, which was designed by Kevin Ashley for his Ph.D dissertation in 1987 at the University of Massachusetts Amherst under the supervision of Edwina Rissland.