Contents
Which is the first question answering dataset?
Presented by Google, this dataset is the first to replicate the end-to-end process in which people find answers to questions. It contains 300,000 naturally occurring questions, along with human-annotated answers from Wikipedia pages, to be used in training QA systems.
Which is the best QA dataset to use?
Shaping Answers with Rules through Conversations (ShARC) is a QA dataset which requires logical reasoning, elements of entailment/NLI and natural language generation. The dataset consists of 32k task instances based on real-world rules and crowd-generated questions and scenarios. Get the dataset here.
What is the tyDi QA question answering dataset?
TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs.
How is information retrieval performed in a QA system?
The information-retrieval process in QA systems is broken down into three stages: question processing, ranking, and answer extraction. Question processing and ranking can be performed using algorithmic functions or machine learning.
What is the Stanford question answering dataset squad?
Data Collection: The Stanford Question Answering Dataset (SQuAD) is a dataset designed for reading comprehension tasks. Crowd workers are employed to ask questions over a set of Wikipedia articles. They are then asked to annotate the questions with the text segment from the article that forms the answer.
Which is an example of a question answering system?
There is another type of datasets, where the answer to the question is not in the context. An example of such dataset type is MS Marco. There is 1M+ Bing user query, 160К+ Answers. Here is a list of good NN architectures for Question Answering that can be helpful in creating a question answering system.
How are datasets sorted by year of publication?
Datasets are sorted by year of publication. Data Collection: Berant et al. use the Google Suggest API as basis for generating questions.