These are questions that require finding and reasoning over multiple supporting documents to answer, the questions are diverse and not constrained to any pre-existing knowledge bases or knowledge schemas, sentence-level supporting facts required for reasoning, allowing QA systems to reason with strong supervision and explain predictions and a new type of factoid comparison questions to test QA systems’ ability to extract relevant facts and perform necessary comparison.ĮLI5 (Explain Like I’m Five) is a longform question answering dataset. HOTPOTQA is a dataset which contains 113k Wikipedia-based question-answer pairs with four key features. The dataset contains 127,000+ questions with answers collected from 8000+ conversations. The goal of the CoQA challenge is to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation. 4| Conversational Question Answering (Coca)Ĭonversational Question Answering (CoQA), pronounced as Coca is a large-scale dataset for building conversational question answering systems. Information-seeking QA dialogs which include 100K QA pairs in total. In this dataset, instances consist of an interactive dialogue between two crowd workers which is a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and a teacher who answers the questions by providing short excerpts (spans) from the text. Question Answering in Context (QuAC) is a dataset for modeling, understanding, and participating in information seeking dialog. Furthermore, researchers added 16,000 examples where answers (to the same questions) are provided by 5 different annotators which will be useful for evaluating the performance of the learned QA systems. It contains 300,000 naturally occurring questions, along with human-annotated answers from Wikipedia pages, to be used in training QA systems. Presented by Google, this dataset is the first to replicate the end-to-end process in which people find answers to questions. Natural Questions (NQ) is a new, large-scale corpus for training and evaluating open-domain question answering systems. The dataset was presented by researchers at Stanford University and SQuAD 2.0 contains more than 100,000 questions. Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset which includes questions posed by crowd-workers on a set of Wikipedia articles and the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. In this article, we list down 10 Question-Answering datasets which can be used to build a robust chatbot. Question answering systems provide real-time answers that are essential and can be said as an important ability for understanding and reasoning. One of the ways to build a robust and intelligent chatbot system is to feed question answering dataset during training the model.
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