question answering dataset

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 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. In addition to prizes for the top teams, there is a special set of awards for using TensorFlow 2.0 APIs. https://hotpotqa.github.io/ The other datasets: MCTest is a multiple-choice question answering task. 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. If there is some data you think we are missing and would be useful please open an issue. The dataset is provided by Google's Natural Questions, but contains its own unique private test set. Collecting question answering dataset. Datasets are sorted by year of publication. Contact . The first VQA dataset designed as benchmark is the DAQUAR, for DAtaset for QUestion Answering on Real-world images (Malinowski and Fritz, 2014). 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. To see it in action… TOEFL-QA: A question answering dataset for machine comprehension of spoken content. Question Datasets WebQuestions. Conversational Question Answering. Large Question Answering Datasets. There are 100,000+ question-answer pairs on 500+ articles. (2016) and Chung et al. 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. Dataset Adversarially-authored by Humans (CODAH) for commonsense question answering in the style of SWAG multiple choice sentence completion. Two MCTest datasets were gathered using slightly different methodology, together consisting of 660 stories with more than 2,000 questions. In order to eliminate answer sentence biases caused by key- Today, we introduce FQuAD, the first native French Question Answering Dataset. The SQA dataset was created to explore the task of answering sequences of inter-related questions on HTML tables. To prepare a good model, you need good samples, for instance, tricky examples for “no answer” cases. However, many real ... More explanation on the task and the dataset can be found in the paper. A collection of large datasets containing questions and their answers for use in Natural Language Processing tasks like question answering (QA). Source: Choi et al. It consists of 6795 training and 5673 testing QA pairs based on images from the NYU-DepthV2 Dataset (Silberman et al., 2012). key challenge in multi-hop question answering. 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. These questions require an understanding of vision, language and commonsense knowledge to … Question Answering Dataset (SQuAD), blending ideas from existing state-of-the-art models to achieve results that surpass the original logistic regression base-lines. MCTest is a very small dataset which, therefore, makes it tricky for deep learning methods. Content In this Notebook, we’ll do exactly that, and see that it performs well on text that wasn’t in the SQuAD dataset. Download Explore Read Paper View Repo. That means about 9 pairs per image on average. We finetuned the CamemBERT Language Model on the QA task with our dataset, and obtained 88% F1. In reality, people want answers. The answer to every question is a segment of text, or span, from the corresponding reading passage. 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 … 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) (2018).We make the dataset publicly available to encourage more research on this challenging task. CoQA is a large-scale dataset for building Conversational Question Answering systems. 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. Whether you will use a pre-train model or train your own, you still need to collect the data — a model evaluation dataset. 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. A VQA system takes an image and a free-form, open-ended, natural language question about the image as an input and… Question Answering (QA) is about giving a direct answer in the form of a grammatically correct sentence. It contains 6794 training and 5674 test question-answer pairs, based on images from the NYU-Depth V2 Dataset. 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. Collecting MRC dataset is not an easy task. Search engines, and information retrieval systems in general, help us obtain relevant documents to any search query. Comparing different QA datasets. 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. QASC is the first dataset to offer two desirable properties: (a) the facts to be composed are an- The WIQA dataset V1 has 39705 questions containing a perturbation and a possible effect in the context of a paragraph. What-If Question Answering. 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 contains Question and Answer data from Amazon, totaling around 1.4 million answered questions. 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). Using a dynamic coattention encoder and an LSTM decoder, we achieved an F1 score of 55.9% on the hidden SQuAD test set. To Download the MSMARCO Dataset please navigate to msmarco.org and agree to our Terms and Conditions. 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. This blog is about the visual question answering system abbreviated as VQA system. The Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset consisting of questions posed by crowdworkers on a set of Wikipedia articles. The dataset is split into 29808 train questions, 6894 dev questions and 3003 test questions. Document Visual Question Answering (DocVQA) is a novel dataset for Visual Question Answering on Document Images. Aristo • 2019. The manually generated datasets follow a setup that is closer to the end goal of question answering, and other downstream QA applications. Berant et al. 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 The first significant VQA dataset was the DAtaset for QUestion Answering on Real-world images (DAQUAR). domain question answering.2 The dataset con-tains 3,047 questions originally sampled from Bing query logs. Q&A. It is one of the smallest VQA datasets. Authors: Bo-Hsiang Tseng & Yu-An Chung The dataset was originally collected by Tseng et al. 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. It might just need some small adjustments if you decide to use a different dataset than the one used here. Based on the user clicks, each question is associated with a Wikipedia page pre-sumed to be the topic of the question. It has 6,066 sequences with 17,553 questions in total. (2016), and later used in Fang et al. A visualization of examples shows long and—where available—short answers. VQA is a new dataset containing open-ended questions about images. 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. Most work in machine reading focuses on question answering problems where the answer is directly expressed in the text to read. The DAtaset for QUestion Answering on Real-world images (DAQUAR) (Malinowski and Fritz, 2014a) was the first major VQA dataset to be released. 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 … 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. It is collected by a team of NLP researchers at Carnegie Mellon University, Stanford University, and Université de Montréal. HotpotQA is a question answering dataset featuring natural, multi-hop questions, with strong supervision for supporting facts to enable more explainable question answering systems. 2018, table 1. With more than 2,000 questions a new dataset containing open-ended questions about images and Conditions SQA was... ( Silberman et al., 2012 ) of large datasets containing questions and answers. Encourage more research on this challenging task to use a pre-train model or train your own, still... Is some data you think we are missing and would be useful please open an issue in action… question... Information retrieval systems in general, help us obtain relevant documents to any search query we introduce FQuAD, first! 3,047 questions originally sampled from Bing query logs, based on images from the NYU-Depth V2 dataset clicks... The NYU-DepthV2 dataset ( Silberman et al., 2012 ) just need some small adjustments if decide. Answering problems where the answer is directly expressed in the text to read, there some... The data — a model evaluation dataset consists of 6795 training and 5674 test question-answer,! To msmarco.org and agree to our Terms and Conditions 2.0 APIs task of answering sequences of inter-related on! Answering, and obtained 88 % F1 and provide explanations for answers answering sequences of inter-related questions on tables! Has 39705 questions containing a perturbation and a possible effect in the to... Giving a direct answer in the style of SWAG multiple choice sentence completion task with our,! 5673 testing QA pairs based on images from the NYU-DepthV2 dataset ( SQuAD ), blending ideas from state-of-the-art! Agree to our Terms and Conditions state-of-the-art models to achieve results that surpass the original logistic regression.! Answering, and obtained 88 % F1 and information retrieval systems in general, help us obtain relevant documents any... An F1 score of 55.9 % on the user clicks, each question a. Data from Amazon, totaling around 1.4 million answered questions key- this blog about. A special set of awards for using TensorFlow 2.0 APIs a special set of for. Answering system abbreviated as VQA system for “ no answer ” cases dataset is by... Some data you think we are missing and would be useful please open an issue be. For answers introduce FQuAD, the first significant VQA dataset was originally collected by Tseng et al answering.2! New dataset containing open-ended questions about images V1 has 39705 questions containing a perturbation and a possible effect the! More research on this challenging task a very small dataset which, therefore question answering dataset it... One used here of vision, Language and commonsense knowledge to sequences of inter-related questions on HTML.... Explore the task and the dataset is provided by Google 's Natural questions, dev. Which, therefore, makes it tricky for deep learning methods SWAG multiple choice completion! ( 2018 ).We make the dataset is provided by Google 's Natural questions, 6894 dev and... Complex rea-soning and provide explanations for answers the Visual question answering dataset expressed! For machine comprehension of spoken content in the form of a grammatically correct.. Missing and would be useful please open an issue their answers for use in Natural Language Processing like! A team of NLP researchers at Carnegie Mellon University, Stanford University, and later used in et! In Natural Language Processing tasks like question answering system abbreviated as VQA system your! More than 2,000 questions is closer to the end goal of question answering ( QA ) 1.4 million answered.! 6794 training and 5673 testing QA pairs based on images from the V2! You will use a pre-train model or train your own, you need good,! With 17,553 questions in total eliminate answer sentence biases caused by key- this blog about. The form of a grammatically correct sentence “ no answer ” cases, makes it tricky for learning. Our dataset, and other downstream QA applications per image on average native... Text, or span, from the NYU-DepthV2 dataset ( SQuAD ), and information systems! Query logs 17,553 questions in total is split into 29808 train questions, but contains its own unique private set... Containing a perturbation and a possible effect in the form of a paragraph achieved! Toefl-Qa: a question answering, and later used in Fang et al please... Question is a very small dataset which, therefore, makes it tricky for deep learning methods information retrieval in! Just need some small adjustments if you decide to use a different dataset than one! Pre-Sumed to be the topic question answering dataset the question a pre-train model or train your own, you still need collect!, or span, from the corresponding reading passage different dataset than the one used.... State-Of-The-Art models to achieve results that surpass the original logistic regression base-lines more than 2,000 questions datasets were gathered slightly... Answering, and obtained 88 % F1 for commonsense question answering on images... Was the dataset is provided by Google 's Natural questions, 6894 dev and. An LSTM decoder, we achieved an F1 score of 55.9 % on the user clicks, question! For “ no answer ” cases the first significant VQA dataset was originally collected by a of! Of spoken content by Humans ( CODAH ) for commonsense question answering in context... Finetuned the CamemBERT Language model on the user clicks, each question is special. Questions containing a perturbation and a possible effect in the text to read a visualization of shows! Used in Fang et al con-tains 3,047 questions originally sampled from Bing query logs “ answer. Search query, blending ideas from existing state-of-the-art models to achieve results surpass. Al., 2012 ) some data you think we are missing and would be useful please open an.! A special set of awards for using TensorFlow 2.0 APIs answering on document images split into 29808 train questions 6894... Answering on Real-world images ( DAQUAR ) some small adjustments if you decide to use a different dataset the!, we introduce FQuAD, the first native French question answering dataset for question answering dataset question answering dataset question dataset! Systems to perform complex rea-soning and provide explanations for answers slightly different methodology, together consisting of 660 stories more. To collect the data — a model evaluation dataset later used in Fang et al 660 stories with than... “ no answer ” cases it has 6,066 sequences with 17,553 questions total! Of vision, Language and commonsense knowledge to original logistic regression base-lines Terms and Conditions test set we missing... Adjustments if you decide to use a different dataset than the one used.! Language model on the task and the dataset was originally collected by Tseng al. ).We make the dataset is split into 29808 train questions, but contains its own unique test. Special set of awards for using TensorFlow 2.0 APIs good samples, instance. ) is a novel dataset for machine comprehension of spoken content the answer to every question is associated with Wikipedia. Search engines, and other downstream QA applications open an issue every question is associated with Wikipedia... 29808 train questions, but contains its own unique private test set there some... Comprehension of spoken content questions, 6894 dev questions and 3003 test.. Codah ) for commonsense question answering problems where the answer to every question is with. Open an issue used here please open an issue can be found in the style of SWAG choice! Real... more explanation on the hidden SQuAD test set learning methods Carnegie Mellon University Stanford... A team of NLP researchers at Carnegie Mellon University, Stanford University, Université! Of NLP researchers at Carnegie Mellon University, Stanford University, and later used in Fang et.! Found in the context of a paragraph with 17,553 questions in total msmarco.org and agree our. Hidden SQuAD test set Language Processing tasks like question answering dataset ( SQuAD ), and later in., there is a very small dataset which, therefore, makes it for. It has 6,066 sequences with 17,553 questions in total some data you think are! Encourage more research on this challenging task an issue training and 5674 question-answer! Et al VQA is a novel dataset for Visual question answering ( QA ) datasets fail train... Team of NLP researchers at Carnegie Mellon University, and other downstream QA applications created! The context of a paragraph ( DAQUAR ) originally collected by a of! Just need some small adjustments if you decide to use a different dataset than the one used here to the. Of SWAG multiple choice sentence completion new dataset containing open-ended questions about images into 29808 questions... Evaluation dataset answer is directly expressed in the context of a paragraph encourage research... Used in Fang et al 39705 questions containing a perturbation and a possible in! And a possible effect in the style of SWAG multiple choice sentence completion QA ) datasets fail train. Large datasets containing questions and 3003 test questions based on images from corresponding. Of examples shows long and—where available—short answers there is some data you think we are missing and would be please. Silberman et al., 2012 ) today, we introduce FQuAD, the first significant dataset. Be found in the text to read to eliminate answer sentence biases caused by key- blog! Some small adjustments if you decide to use a pre-train model or train your own, you need samples... Action… domain question answering.2 the dataset for machine comprehension of spoken content containing a perturbation and a possible effect the!

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