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Question Answering/Machine Reading Comprehension 2020 [335] Nguyen et al. Open-Domain Question Answering Goes Conversational via Question Rewriting An end-to-end open-domain question answering. This dataset includes 14,000 conversations with 81,000 question-answer pairs. Context, Question, Rewrite, Answer, Answer_URL, Conversation_no, Turn_no ...
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]
Semantic Parsing for Conversational Question Answering. A standard dataset for question answering via semantic parsing is the Air Travel Information System (ATIS) dataset, which contains questions and commands about upcoming flights as well as corresponding SQL. [30]
Question answering systems in the context of [vague] machine reading applications have also been constructed in the medical domain, for instance related to [vague] Alzheimer's disease. [3] Open-domain question answering deals with questions about nearly anything and can only rely on general ontologies and world knowledge. Systems designed for ...
Haystack is an open source Python framework for building custom applications with large language models. With its modular building blocks, software developers can implement pipelines to address various search tasks over large document collections, such as document retrieval, semantic search, text generation, question answering, or summarization.
The set of images in the MNIST database was created in 1994. Previously, NIST released two datasets: Special Database 1 (NIST Test Data I, or SD-1); and Special Database 3 (or SD-2). They were released on two CD-ROMs. SD-1 was the test set, and it contained digits written by high school students, 58,646 images written by 500 different writers.
Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. The data may also be collected from sensors in the environment, including traffic cameras, satellites, recording devices, etc.
The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning functionalities. [4]