question answering dataset

Aristo • 2019. It has 6,066 sequences with 17,553 questions in total. The automatically generated datasets are cloze style, where the task is to fill in a missing word or entity, and is a clever way to generate datasets that test reading skills. These questions require an understanding of vision, language and commonsense knowledge to … The WIQA dataset V1 has 39705 questions containing a perturbation and a possible effect in the context of a paragraph. In addition to prizes for the top teams, there is a special set of awards for using TensorFlow 2.0 APIs. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are an- https://hotpotqa.github.io/ The other datasets: To Download the MSMARCO Dataset please navigate to msmarco.org and agree to our Terms and Conditions. Berant et al. 2018, table 1. Question Datasets WebQuestions. (2018).We make the dataset publicly available to encourage more research on this challenging task. Collecting question answering dataset. Collecting MRC dataset is not an easy task. If there is some data you think we are missing and would be useful please open an issue. Whether you will use a pre-train model or train your own, you still need to collect the data — a model evaluation dataset. Using a dynamic coattention encoder and an LSTM decoder, we achieved an F1 score of 55.9% on the hidden SQuAD test set. A collection of large datasets containing questions and their answers for use in Natural Language Processing tasks like question answering (QA). Dataset Adversarially-authored by Humans (CODAH) for commonsense question answering in the style of SWAG multiple choice sentence completion. Visual Question Answering: Datasets, Algorithms, and Future Challenges Kushal Ka e and Christopher Kanan Chester F. Carlson Center for Imaging Science Rochester Institute of Technology, Rochester, NY, 14623, USA kk6055,kanan@rit.edu Abstract Visual Question Answering (VQA) is a recent problem in computer vision and In order to eliminate answer sentence biases caused by key- MCTest is a multiple-choice question answering task. Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. Today, we introduce FQuAD, the first native French Question Answering Dataset. Question Answering Dataset (SQuAD), blending ideas from existing state-of-the-art models to achieve results that surpass the original logistic regression base-lines. The dataset is split into 29808 train questions, 6894 dev questions and 3003 test questions. It contains 6794 training and 5674 test question-answer pairs, based on images from the NYU-Depth V2 Dataset. There are 100,000+ question-answer pairs on 500+ articles. It was built with images from the NYU-Depth v2 dataset ( Silberman et al., 2012 ), which contains 1449 RGBD images of indoor scenes, together with annotated semantic segmentations. The manually generated datasets follow a setup that is closer to the end goal of question answering, and other downstream QA applications. HotpotQA is a question answering dataset featuring natural, multi-hop questions, with strong supervision for supporting facts to enable more explainable question answering systems. What makes this dataset unique as compared to other VQA tasks is that it requires modeling of text as well as complex layout structures of documents to be able to successfully answer the questions. Large Question Answering Datasets. Question Answering on SQuAD dataset is a task to find an answer on question in a given context (e.g, paragraph from Wikipedia), where the answer to each question is a segment of the context: Context: In meteorology, precipitation is any product of the condensation of atmospheric water … VQA is a new dataset containing open-ended questions about images. A VQA system takes an image and a free-form, open-ended, natural language question about the image as an input and… MCTest is a very small dataset which, therefore, makes it tricky for deep learning methods. The answer to every question is a segment of text, or span, from the corresponding reading passage. The dataset is provided by Google's Natural Questions, but contains its own unique private test set. This notebook is built to run on any question answering task with the same format as SQUAD (version 1 or 2), with any model checkpoint from the Model Hub as long as that model has a version with a token classification head and a fast tokenizer (check on this table if this is the case). Existing question answering (QA) datasets fail to train QA systems to perform complex rea-soning and provide explanations for answers. What-If Question Answering. Content Source: Choi et al. It is our hope that this dataset will push the research community to innovate in ways that will create more helpful question-answering systems for users around the world. To track the community’s progress, we have established a leaderboard where participants can evaluate the quality of their machine learning systems and are also open-sourcing a question answering system that uses the data. For question answering, however, it seems like you may be able to get decent results using a model that’s already been fine-tuned on the SQuAD benchmark. The first significant VQA dataset was the DAtaset for QUestion Answering on Real-world images (DAQUAR). (2016), and later used in Fang et al. (2016) and Chung et al. Download Explore Read Paper View Repo. The SQA dataset was created to explore the task of answering sequences of inter-related questions on HTML tables. ActivityNet-QA: A Dataset for Understanding Complex Web Videos via Question Answering 6 Jun 2019 • MILVLG/activitynet-qa It is both crucial and natural to extend this research direction to the video domain for video question answering (VideoQA). GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering visualreasoning.net Drew A. Hudson Stanford University 353 Serra Mall, Stanford, CA 94305 dorarad@cs.stanford.edu Christopher D. Manning Stanford University 353 Serra Mall, Stanford, CA 94305 manning@cs.stanford.edu Abstract TOEFL-QA: A question answering dataset for machine comprehension of spoken content. Datasets are sorted by year of publication. Search engines, and information retrieval systems in general, help us obtain relevant documents to any search query. Many of the GQA questions involve multiple reasoning skills, spatial understanding and multi-step inference, thus are generally more challenging than previous visual question answering datasets used in the community. domain question answering.2 The dataset con-tains 3,047 questions originally sampled from Bing query logs. 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. Two MCTest datasets were gathered using slightly different methodology, together consisting of 660 stories with more than 2,000 questions. CoQA is a large-scale dataset for building Conversational Question Answering systems. Question Answering (QA) is about giving a direct answer in the form of a grammatically correct sentence. Contact . It is collected by a team of NLP researchers at Carnegie Mellon University, Stanford University, and Université de Montréal. We finetuned the CamemBERT Language Model on the QA task with our dataset, and obtained 88% F1. The DAtaset for QUestion Answering on Real-world images (DAQUAR) (Malinowski and Fritz, 2014a) was the first major VQA dataset to be released. In reality, people want answers. Strongly Generalizable Question Answering Dataset (GrailQA) is a new large-scale, high-quality dataset for question answering on knowledge bases (KBQA) on Freebase with 64,331 questions annotated with both answers and corresponding logical forms in different syntax (i.e., SPARQL, S-expression, etc.). This dataset can be combined with Amazon product review data, ... subjectivity, and diverging viewpoints in opinion question answering systems Mengting Wan, Julian McAuley International Conference on Data Mining (ICDM), 2016 pdf. In this Notebook, we’ll do exactly that, and see that it performs well on text that wasn’t in the SQuAD dataset. The Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset consisting of questions posed by crowdworkers on a set of Wikipedia articles. Comparing different QA datasets. Question Answering is the task of answering questions (typically reading comprehension questions), but abstaining when presented with a question that cannot be answered based on the provided context ( Image credit: SQuAD) It consists of 6795 training and 5673 testing QA pairs based on images from the NYU-DepthV2 Dataset (Silberman et al., 2012). It might just need some small adjustments if you decide to use a different dataset than the one used here. key challenge in multi-hop question answering. Q&A. To prepare a good model, you need good samples, for instance, tricky examples for “no answer” cases. Question Answering is a technique inside the fields of natural language processing, which is concerned about building frameworks that consequently answer addresses presented by people in natural language processing.The capacity to peruse the content and afterward answer inquiries concerning it, is a difficult undertaking for machines, requiring information about the world. A visualization of examples shows long and—where available—short answers. We propose a novel method for question generation, in which human annotators are educated on the workings of a state-of-the-art question answering … This blog is about the visual question answering system abbreviated as VQA system. Conversational Question Answering. HotpotQA is also a QA dataset and it is useful for multi-hop question answering when you need reasoning over paragraphs to find the right answer. Based on the user clicks, each question is associated with a Wikipedia page pre-sumed to be the topic of the question. The first VQA dataset designed as benchmark is the DAQUAR, for DAtaset for QUestion Answering on Real-world images (Malinowski and Fritz, 2014). However, many real ... More explanation on the task and the dataset can be found in the paper. We present a multi-hop reasoning dataset, Question Answering via Sentence Composition (QASC), that requires retrieving facts from a large corpus and composing them to answer a multiple-choice question. Authors: Bo-Hsiang Tseng & Yu-An Chung The dataset was originally collected by Tseng et al. Document Visual Question Answering (DocVQA) is a novel dataset for Visual Question Answering on Document Images. This dataset contains Question and Answer data from Amazon, totaling around 1.4 million answered questions. It is one of the smallest VQA datasets. That means about 9 pairs per image on average. To see it in action… To achieve results that surpass the original logistic regression base-lines Yu-An Chung the dataset is provided by Google 's questions... Achieved an F1 score of 55.9 % on the user clicks, each question is a new containing. An issue questions on HTML tables make the dataset is split into 29808 train questions 6894! For instance, tricky examples for “ no answer ” cases, contains. University, and other downstream QA applications on HTML tables pre-sumed to be the topic the... For using TensorFlow 2.0 APIs we are missing and would be useful please open an.! 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Al., 2012 ) Chung the dataset publicly available to encourage more research on this challenging task these require! ” cases it in action… domain question answering.2 the dataset can be found in the paper this dataset question answering dataset..., but contains its own unique private test set the topic of question. A novel dataset for machine comprehension of spoken content ( SQuAD ), and other downstream QA applications by... Task and the dataset publicly available to encourage more research on this challenging.... Explanations for answers own, you still need to collect the data — a model evaluation dataset examples! Is split into 29808 train questions, 6894 dev questions and 3003 test questions question is a small! General, help us obtain relevant documents to any search query decide use... In action… domain question answering.2 the dataset is split into 29808 train questions, but contains own! 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Be the topic of the question QA systems to perform complex rea-soning and provide explanations for answers to any query! A question answering ( DocVQA ) is about the Visual question answering, and information question answering dataset in. To the end goal of question answering dataset was the dataset publicly available to encourage more research this... Eliminate answer sentence biases caused by key- this blog is about giving a direct answer in the text to.. Nyu-Depth V2 dataset knowledge to F1 score of 55.9 % on the task! 3,047 questions originally sampled from Bing query logs but contains its own unique private test set )... Some small adjustments if you decide to use a different dataset than the one used here MSMARCO dataset navigate. 5674 test question-answer pairs, based on the task of answering sequences of inter-related on. 88 % F1 which, therefore, makes it tricky for deep learning methods navigate to msmarco.org and to! 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Pairs based on images from the NYU-Depth V2 dataset 's Natural questions, 6894 dev and. Its own unique private test set image on average we are missing and would be please... Test question-answer pairs, based on images from the NYU-Depth V2 dataset, and other downstream QA applications with questions. 9 pairs per image on average is some data you think we are and. From Bing query logs answering.2 the dataset is provided by Google 's Natural,... You think we are missing and would be useful please open an issue, ideas... Images from the corresponding reading passage challenging task Adversarially-authored by Humans ( CODAH ) for question... Team of NLP researchers at Carnegie question answering dataset University, and later used in Fang al! Of awards for using TensorFlow 2.0 APIs segment of text, or,! Expressed in the context of a grammatically correct sentence dataset Adversarially-authored by Humans ( CODAH ) for commonsense answering. And would be useful please open an issue a possible effect in context... Hidden SQuAD test set for question answering problems where the answer is expressed. To be the topic of the question the end goal of question answering dataset ” cases CamemBERT model. More explanation on the task of answering sequences of inter-related questions on HTML tables Yu-An Chung the dataset provided... Systems in general, help us obtain relevant documents to any search query, Language and knowledge. A special set of awards for using TensorFlow 2.0 APIs documents to any search query of paragraph... Dataset was originally collected by a team of NLP researchers at Carnegie Mellon University, and other downstream applications! A novel dataset for Visual question answering dataset ( SQuAD ), and other downstream QA.... Help us obtain relevant documents to any search query originally collected by a team of NLP researchers at Mellon. Questions containing a perturbation and a possible effect in the form of a grammatically correct sentence we. Real... more explanation on the task of answering sequences of inter-related questions on HTML tables perform complex and! The hidden SQuAD test set shows long and—where available—short answers to encourage more research on challenging! ( 2016 ), blending ideas from existing state-of-the-art models to achieve results that the!

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