However, existing numerical reasoning datasets seldom explicitly indicate the formulas employed during the reasoning steps. In particular, our framework revises the extracted The Dataset. MultiModalQA is a challenging question answering dataset that requires joint reasoning over text, tables and images, consisting of 29,918 examples. This dataset contains 10,960 questions posed by Python learners at different learning stages and types. The model answers the question only using the information given. Question Answering in Context is a dataset for modeling, understanding, and participating in information seeking dialog. The reading sections in SQuAD are taken 5 days ago · In this paper, we construct a novel dataset XQA for cross-lingual OpenQA research. We evaluate the state-of-the-art QA models trained using existing QA datasets on NOAHQA and show that the best among them can only achieve 55. The M3C task builds on the popular Visual Question Answering (VQA) and Machine Comprehension (MC) paradigms by framing question answering as a machine comprehension task, where the context needed to answer questions is provided and composed of both text and images. Jun 10, 2024 · Abstract. To bridge this gap, we propose a question answering dataset for formula-based numerical reasoning called FormulaQA, from junior high school physics examinations This repository contains dataset for ODSQA: Open-domain Spoken Question Answering Dataset. open-domain QA). In other document-based question answering datasets that focus on answer extraction, the answer to a given question occurs in multiple documents. ⚡⚡ If you’d like to save inference time, you can first use passage ranking models to see which The WebQuestions dataset is a question answering dataset using Freebase as the knowledge base and contains 6,642 question-answer pairs. TheoremQA is curated by domain experts containing 800 high-quality questions covering 350 theorems1 from Math, Physics, EE&CS, and Finance. This survey aims to explore and shed light upon the recent and most powerful deep learning-based Nov 11, 2021 · Complex Knowledge Base Question Answering is a popular area of research in the past decade. Ayu Purwarianti, Masatoshi Tsuchiya, and Seiichi Nakagawa. Can you use NLP to answer these questions? Sep 27, 2019 · TWEETQA is a social media-focused question answering dataset. The app provides a variety of editing tools, including exposure adjustments, color correction, and cropping. We presented RuBQ – the first Russian dataset for Question Answering over Wikidata. We can see the training, validation and test sets all have SimpleQuestions is a large-scale factoid question answering dataset. S tanford Qu estion A nswering D ataset (SQuAD) is a new reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage. oremQA, the first theorem-driven question-answering dataset designed to evaluate AI models’ capabilities to apply theorems to solve challenging science problems. 5 exact match scores, while the human performance is 89. Strongly Generalizable Question Answering (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. Sourced from online question answering community forums, we call it VQAonline. The dialogs involve two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts from the text. It yields a more challenging table QA setting because it requires generating free-form text answers after retrieval, inference, and integration of multiple discontinuous facts from a structured knowledge source. 0 (default): Fixes issue with small Introduced by Chen et al. The dataset is split into 29808 train questions, 6894 dev questions and 3003 test questions. We characterize this dataset and how it relates to eight mainstream VQA datasets. Data instances consist of an interactive dialog between two crowd workers: (1) a student who poses a sequence of freeform questions to learn as much as possible about a hidden Wikipedia text, and (2) a teacher who answers the questions by providing short excerpts (spans FeTaQA is a Free-form Table Question Answering dataset with 10K Wikipedia-based {table, question, free-form answer, supporting table cells} pairs. Supports careful analysis based on question and answer type, length, number of reasoning steps and difficulty. However, such assumption might be sub-optimal as the real-world knowledge is distributed over heterogeneous forms. These questions require an understanding of vision, language and commonsense knowledge to answer. This is an Chinese dataset. After data acquisition, we provide rigorous annotation guidelines in an iterative process and then the annotation of question-answer pairs in SemEvalCQA format. Refresh. It is a large-scale dataset for building Conversational Question Answering Systems. SyntaxError: Unexpected token < in JSON at position 4. We provide an Visual Question Answering (VQA) is a dataset containing open-ended questions about images. However, currently no such annotated dataset is publicly available, which hinders the development of neural question-answering (QA) systems. With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger May 15, 2021 · CoQA is a Conversational Question Answering dataset released by Stanford NLP in 2019. 1 with 107,785 answerable questions and SQuAD 2. Due to the questions’ and answers’ ambiguity, datasets like this are treated as a multi-label classification problem (as multiple answers are possibly valid). We propose a new method for automatically deriving NLI datasets from the growing abundance of large-scale question answering datasets. Since you are giving a context alongwith the question to the model, this is called an open-book QA task. Each fact is a triple (subject, relation, object) and the answer to the question is always the object. , For example, certain reading comprehension questions containing direct references such as "according to the passage" or dangling mentions such as "what did she say?" would not be considered quiz-style. HotpotQA is a question answering dataset collected on the English Wikipedia, containing about 113K crowd-sourced questions that are constructed to require the introduction paragraphs of two Wikipedia articles to answer. Document question answering models take a (document, question) pair as input and return an answer in natural language. Preview: A simple tool to view samples in the dataset is provided here . Jan 28, 2022 · Abstract. TheoremQA is curated by domain experts containing 800 high-quality questions covering 350 theorems (e. gov, GARD, MedlinePlus Health Topics). Besides, we provide several baseline systems for cross-lingual OpenQA, including two machine translation-based methods and one zero-shot cross-lingual method Mar 29, 2018 · Stanford Question Answering Dataset is a new reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage. gov, niddk. A English dataset Spoken-SQuAD is also available here . To help advance question answering (QA) and create smarter assistants, Facebook AI is sharing the first large-scale dataset, code, and baseline models for long-form QA, which requires machines to provide long, complex answers — something that existing algorithms have not been challenged to do before. , StackExchange ). from datasets import load_dataset datasets = load_dataset("squad") The datasets object itself is a DatasetDict, which contains one key for the training, validation and test set. analyses of the dataset. TyDi QA is a question answering dataset covering 11 typologically diverse languages with 204K question-answer pairs. Feb 15, 2024 · Multi-span answer extraction, also known as the task of multi-span question answering (MSQA), is critical for real-world applications, as it requires extracting multiple pieces of information from a text to answer complex questions. 20. The original split uses 3,778 examples for training and 2,032 for testing. json and the episode histories can be downloaded by following the instructions here. In this paper, we introduce ProCQA, a large-scale programming question The TQA dataset encourages work on the task of Multi-Modal Machine Comprehension (M3C) task. (i. 3. To see how this works using the current example, we can limit the length to 100 and use a sliding window of 50 tokens. MLQA consists of over 5K extractive QA instances (12K in English) in SQuAD format in seven languages - English, Arabic, German, Spanish, Hindi, Vietnamese and Simplified Chinese. For all the questions, we select answers to other questions of the same category as negative answers. The dataset and code for paper: TheoremQA: A Theorem-driven Question Answering dataset Resources. Methods This paper introduces CoQUAD, a question-answering system that can Apr 12, 2022 · Both these datasets are combined to generate a multimodal QA dataset (DrugEHRQA), which contains question-answers from both structured and unstructured data of MIMIC-III. MLEC-QA is a Chinese multi-choice Biomedical Question Answering Dataset. Thorough Diagnosis. Taylor's theorem, Lagrange's theorem, Huffman May 23, 2018 · SQuAD Dataset. In this paper, we introduce a novel framework that extracts question-worthy phrases from a passage and then generates corresponding questions considering previous conversations. Question answering and reading comprehension have been particularly prolific in this regard, with over 80 new datasets appearing in the past two years. Although different algorithms have continually been proposed and shown success on different Visual question answering (VQA) is a challenging task that reasons over questions on images with knowledge. Discuss a project where you utilized both qualitative and quantitative data from a dataset. nih. 2 Knowledge Graph Question Answering KGQA aims at retrieving the correct answer or respectively answers denoted as of a given natural language question from the knowledge graph KG. Observing that answers in our dataset tend to be much longer (i. The question-answer pairs are available in data/open-eqa-v0. This is not in line with the actual linguistic conventions, which often use a lot of modifiers. in FinQA: A Dataset of Numerical Reasoning over Financial Data. Hariom A. content_copy. Question-Answering Models are machine or deep learning models that can answer questions given some context, and sometimes without any context (e. It consists of three files, one each for three different splits of the dataset named as {split}_qa. It contains 270K complex, diverse questions that require explanatory multi-sentence answers. If the issue persists, it's likely a problem on our side. Retrieval-based code question answering seeks to match user queries in natural language to relevant code snippets. The collection covers 37 question types (e. Each entry can be either an impossible-to-answer or a question with one or more answers spanning in the passage (the context) from which the questioner proposed the question. The AmazonQA dataset is a large review-based Question Answering dataset ( paper ). It contains 117k multiple-choice questions Aug 7, 2023 · In this work, we present SciGraphQA, a synthetic multi-turn question-answer dataset related to academic graphs. A prerequisite for VQA is the availability of annotated datasets, while the available datasets have several limitations. g. HuggingFace. Feb 3, 2023 · Question answering (QA) models often rely on large-scale training datasets, which necessitates the development of a data generation framework to reduce the cost of manual annotations. This field of research has attracted a sudden amount of interest lately due mainly to the integration of the deep learning models in the Question Answering Systems which consequently power up many advancements and improvements. Introducing long-form question answering. For this initial release, 11. 0 with 100,000 answerable and unanswerable questions. The positive question answer pairs are the ground truth pair provided online. Jan 13, 2022 · We will use the 🤗 Datasets library to download the SQUAD question answering dataset using load_dataset(). It contains 127,000+ questions with answers collected from 8000+ conversations on seven diverse domains. Apr 23, 2021 · MultiModalQA: Complex Question Answering over Text, Tables and Images. Recent public datasets have led to encouraging results in this field, but are mostly limited to English and only involve a small number of question types and relations, hindering research in more realistic settings and in languages other than English. This is analogous to open-book exams, where you are allowed to bring a book to the exam. The dataset contains 8,281 financial QA pairs, along with their numerical reasoning processes. The DrugEHRQA dataset has medication-related queries, containing over 70,000 question-answer pairs. Document Question Answering (also known as Document Visual Question Answering) is the task of answering questions on document images. Each question in the dataset comes with the two gold paragraphs, as well as a list of sentences in these paragraphs Jan 29, 2023 · The model then gives you an answer to the question. Stars. MIT license Activity. We introduce Q-Pain, a dataset for assessing bias in medical QA in the context of pain management. In addition, few state-of-the-art KBQA models are A suite of new metrics to evaluate not only accuracy, but also the consistency, validity and plausibility of responses. However, for the specific biomedical domain, QA systems are still immature due to expert-annotated datasets being limited by category and scale. CoQA is a large-scale dataset for building conversational question answering systems. Medical Question Answering Dataset of 47,457 QA pairs created from 12 NIH websites Topics natural-language-processing question-answering medical-informatics clinical-nlp medical-nlp 5 Conclusion and Future Work. Jul 27, 2021 · Alongside huge volumes of research on deep learning models in NLP in the recent years, there has been also much work on benchmark datasets needed to track modeling progress. This dataset is more challenging than standard QA benchmark datasets such as Stanford Question Answering Dataset (SQuAD), as the answers for a question may not be directly obtained by span prediction and the context is very long MLQA (MultiLingual Question Answering) is a benchmark dataset for evaluating cross-lingual question answering performance. , SPARQL, S-expression, etc. SQuAD is challenging. MLQA is highly parallel, with QA instances Aug 24, 2022 · The Stanford Question Answering Dataset ( SQuAD) is a reading comprehension dataset made up of questions posed by crowd workers on a collection of Wikipedia articles, with the response to each question being a text segment, or span, from the relevant reading passage, or the question being unanswerable. In this paper, we present MLEC-QA, the largest-scale The WIQA dataset V1 has 39705 questions containing a perturbation and a possible effect in the context of a paragraph. 0. We manually annotate part of the dataset to ensure correctness. The Natural Questions Dataset. e 5 days ago · In this paper, we introduce TheoremQA, the first theorem-driven question-answering dataset designed to evaluate AI models’ capabilities to apply theorems to solve challenging science problems. answering multi-type complex questions in prac-tice. Yet, compared with ImageQA, VideoQA is largely underexplored and progresses slowly. Answers to customer questions can be drawn from those documents. 1 Introduction Question answering (QA) (Hirschman and Gaizauskas,2001) aims at providing correct answers to questions base on some given context or knowledge. Compare models, papers, and code for question answering evaluation metrics and performance. We further conduct evaluations on LLMs with size ranging from 7B to over 100B parameters utilizing zero-shot and few-shot chain-of-thoughts methods and we explored the approach of Recent advances in transformers have enabled Table Question Answering (Table QA) systems to achieve high accuracy and SOTA results on open domain datasets like WikiTableQuestions and WikiSQL. The dataset consists of 1,500 questions, their machine translations into English, and annotated SPARQL queries. It differs from prior datasets; examples include that it contains: (1) authentic context that clarifies the question, (2) an answer the individual asking the question Oct 8, 2020 · MCTest only has a total of 2,640 questions, and Deep Read only has a total of 600 questions. 152 stars Watchers. Join the 2020 GQA ChallengeforReal-World Visual Reasoning. Previous efforts Aug 21, 2018 · We present QuAC, a dataset for Question Answering in Context that contains 14K information-seeking QA dialogs (100K questions in total). It has two versions: SQuAD 1. It is also the largest open-sourced chart VQA dataset with non-synthetic charts. The usage and amount of information available on the internet increase over the past decade. TyDi QA. Visual Question Answering (VQA) v2. FinQA is a new large-scale dataset with Question-Answering pairs over Financial reports, written by financial experts. Existing table question answering datasets contain abundant factual questions that primarily evaluate a QA system’s comprehension of query and tabular data. Existing open-domain question answering datasets focus on dealing with textual homonogeneous information. This repository comprises: instructions to download and work with the dataset. Question: How do Jellyfish function without brains or nervous systems? With the increase in the number of domestic tourists and the popularity of digital upgrades in attractions, it is crucial to develop a question-answering(QA) system about the details of the attractions. The goal of this dataset is to provide a challenging benchmark for end-to-end conversational question answering that includes the individual subtasks of It contains 4 data fields: question subject, question body, answer and label. The dataset that is used the most as an academic benchmark for extractive question answering is SQuAD (The Stanford Question Answering Dataset). This study is the largest survey of the field to date. ELI5 is a dataset for long-form question answering. e. It was created by crawling questions through the Google Suggest API, and then obtaining answers using Amazon Mechanical Turk. Although several recent studies have aimed to generate synthetic questions with single-span answers, no study has been conducted on the creation of list questions with multiple, non-contiguous spans as answers TriviaQA is a realistic text-based question answering dataset which includes 950K question-answer pairs from 662K documents collected from Wikipedia and the web. With 100,000+ question-answer pairs on 500+ articles, SQuAD Jan 13, 2023 · Stanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. IM-TQA is a Chinese table question answering dataset with 1,200 tables and 5,000 question-answer pairs, which highlights Implicit and Multi-type table structures for real-world TQA scenarios. Download the dataset without explanations, using which the models in the EMNLP 2019 submission were trained and tested (human accuracy Mar 2, 2022 · Video Question Answering (VideoQA) aims to answer natural language questions according to the given videos. Unexpected token < in JSON at position 4. Introduction. Readme License. It can be used to test three levels of May 21, 2023 · In this paper, we introduce TheoremQA, the first theorem-driven question-answering dataset designed to evaluate AI models' capabilities to apply theorems to solve challenging science problems. ,2016) and ComplexWe-bQuestions (Talmor and Berant,2018) mostly fo-cus on multi-hop questions and questions with var-ious constraints, while many question types in real-world KBQA applications are still not covered: e. ELI5 is also a task in Dodecadialogue . To help spur development in open-domain question answering, we have created the Natural Questions (NQ) corpus, along with a challenge website based on this data. who made it to stage 3 in american ninja warrior season 9). 7. SQuAD is a reading Mar 25, 2024 · Abstract. json It consists the following fields: image: The image filename which the given question-answer pair applies to question: Question answer: Answer to the Questions. 6K question-answer pairs are included out of the 20K in the complete dataset. Let’s have a walk-through of the code! Install the transformers library. Questions in MLEC-QA are collected from the National Medical Licensing Examination in China (NMLEC), which are carefully designed by human experts to evaluate professional knowledge and skills for those who want to be medical practitioners in China. First, current KBQA datasets such as Com-plexQuestions (Bao et al. Jul 25, 2019 · NLP. Jun 2, 2022 · Background Due to the growing amount of COVID-19 research literature, medical experts, clinical scientists, and researchers frequently struggle to stay up to date on the most recent findings. Our approach hinges on learning a sentence transformation model which converts question-answer pairs into their declarative forms. It offers a wide range of editing tools, including selective adjustments, curves, filters, text overlays, and a powerful healing brush. Models usually rely on multi-modal features, combining text, position of words (bounding Feb 6, 2021 · MedQuAD (Medical Question Answering Dataset) MedQuAD includes 47,457 medical question-answer pairs created from 12 NIH websites (e. However, almost all short answers (90%) only contain a single span of text. You can access the dataset as well as submit your results to evaluate on Short answers may contain more than one span, if the question is asking for a list of answers (e. To build our dataset, we selected 290,000 Computer Science or Machine Learning ArXiv papers VQAonline is the first VQA dataset in which all contents originate from an authentic use case. Aug 10, 2023 · Question understanding is an important issue to the success of a Knowledge-based Question Answering (KBQA) system. This can be done by learning a transforma-tion function translating the question into a semantically Jun 4, 2020 · The Stanford Question Answering Dataset (SQuAD) is a collection of 100k crowdsourced QA pairs. It is collected by a team of NLP researchers at Carnegie Mellon University, Stanford University, and Université de Montréal. Get the dataset Frequently Asked Questions. 1) The diversity of HotpotQA is a question answering dataset featuring natural, multi-hop questions, with strong supervision for supporting facts to enable more explainable question answering systems. Additional question-answer pairs will Jun 10, 2024 · With NOAHQA, we develop an interpretable reasoning graph as well as the appropriate evaluation metric to measure the answer quality. Treatment, Diagnosis, Side Effects) associated with diseases, drugs and other medical entities such as tests. Despite being primarily Mar 4, 2022 · Question Answering Datasets. The OpenViVQA dataset contains 11,000+ images with 37,000+ question-answer pairs which introduces the Text-based Open-ended Visual Question Answering in Vietnamese. However, the existing study does not pay enough attention to this issue given that the questions in the existing KBQA datasets are usually expressed in simple and straightforward way. All short answers are contained by the long answer given in the same annotation. This repository contains the MultiModalQA dataset, format description, and link to the images file. Web search results are used as evidence documents to answer each question. keyboard_arrow_up. We introduce the first VQA dataset in which all contents originate from an authentic use case. Bhatt. Feb 21, 2024 · To bridge this gap, we propose a question answering dataset for formula-based numerical reasoning called FormulaQA, from junior high school physics examinations. Such transformers are frequently pre-trained on open-domain content such as Wikipedia, where they effectively encode questions and corresponding tables Feb 20, 2024 · The application of formulas is a fundamental ability of humans when addressing numerical reasoning problems. QuAC introduces Aug 30, 2023 · Lastly, if the dataset is structured, I might use a database management system (DBMS) optimized for large datasets. The crowd-sourced dataset consists of more than 9,000 entries. The dataset is divided into training, validation, and test sets with 75,910, 10,845 and 21,687 questions As we saw in Chapter 6 when we explored the internals of the question-answering pipeline, we will deal with long contexts by creating several training features from one sample of our dataset, with a sliding window between them. We also Dec 7, 2021 · Question Answering Survey: Directions, Challenges, Datasets, Evaluation Matrices. SQuAD has these datasets dominated with a whopping 100,000+ questions. Sep 23, 2022 · Conversational question--answer generation is a task that automatically generates a large-scale conversational question answering dataset based on input passages. To this end, we present MASH-QA, a Multiple Persian Question Answering (PersianQA) Dataset is a reading comprehension dataset on Persian Wikipedia. To Nov 27, 2023 · Visual Question Answering (VQA) entails answering questions about images. Moreover, rather than just creating a one-hot encoded vector, one creates a soft encoding, based on the number of times a certain answer appeared in the annotations. Join the Challenge! 5 days ago · Abstract. The dataset now includes 10,898 articles, 17,794 tweets, and 13,757 crowdsourced question-answer pairs. 2. DBMSs like SQL Server or Oracle have built-in mechanisms for handling large amounts of data efficiently. Question Answering (QA) has been successfully applied in scenarios of human-computer interaction such as chatbots and search engines. SciGraphQA is 13 times larger than ChartVQA, the previously largest chart-visual question-answering dataset. This dataset aims to measure the ability of machines to understand a text passage and answer a series of interconnected questions that appear in a conversation. You can use Question Answering (QA) models to automate the response to frequently asked questions by using a knowledge base (documents) as context. The NQ corpus contains questions from real users, and it requires QA systems to read and comprehend an entire Wikipedia article that may or may not Refresh. AmazonQA: A Review-Based Question Answering Task. We introduce QReCC ( Q uestion Re writing in C onversational C ontext), an end-to-end open-domain question answering dataset comprising of 14K conversations with 81K question-answer pairs. It has earned increasing attention with recent research trends in joint vision and language understanding. However, restricted by their short-form answers, these datasets fail to include question–answer interactions that represent more advanced and naturally occurring information needs: questions that ask for reasoning and The question-answer pair can be downloaded from this url. Feb 23, 2024 · In existing related works, datasets for diverse Python programming learners are still scarce. To address this challenge, this paper introduces a specialized Chinese single-turn question-and-answer (Q&A) dataset for Python learners. It consists of 108,442 natural language questions, each paired with a corresponding fact from Freebase knowledge base. It consists of a training set in English as well as development and test sets in eight other languages. It is sourced from an online question answering community (i. This digitization leads to the need for automated answering system to extract fruitful information from redundant and transitional Oct 1, 2020 · LiveQA, a new question answering dataset constructed from play-by-play live broadcast, contains 117k multiple-choice questions written by human commentators for over 1,670 NBA games, which are collected from the Chinese Hupu1 website. While previous QA datasets usually focus on news domain like CNN/DAILYMAIL and NewsQA Sep 9, 2018 · Existing datasets for natural language inference (NLI) have propelled research on language understanding. PerCQA contains 989 questions and 21,915 annotated answers. TheoremQA is curated by domain experts containing 800 high-quality questions covering 350 theorems from Math, Physics, EE&CS, and Finance. Question Answering is a crucial natural language processing task. Answering questions in many real-world applications often requires complex and precise information excerpted from texts spanned across a long document. In this paper, we introduce LiveQA, a new question answering dataset constructed from play-by-play live broadcast. VSCO: VSCO is known for its artistic filters and film-like presets. However, there is little work on attractions QA, and the main bottleneck is the lack of available datasets. New Reading Comprehension Dataset on 100,000+ Question-Answer Pairs. In this paper, we simulate a more challenging setting where we don't know in advance where the answer of a Jun 1, 2021 · There are six categories of questions: date, location, name, organization, person, and quantitative. cancer. Despite the active studies and rapid progress in English MSQA research, there is a notable lack of publicly available MSQA benchmark in Chinese. 300 RuBQ questions are unanswerable, which poses a new challenge for KBQA systems and makes the task more realistic. Previous approaches typically rely on pretraining models using crafted bi-modal and uni-modal datasets to align text and code representations. SQuAD is a collection of question-answer pairs derived from Wikipedia articles, where the answers can be any sequence of tokens in the text. They can extract answer phrases from paragraphs, paraphrase the answer generatively, or choose one option out of a list of given options, and so on. We evaluate a wide spectrum of 16 large By Rohit Kumar Singh. 3 days ago · This dataset contains the questions and answers crawled from the most well-known Persian forum. There is a pressing need to assist researchers and practitioners in mining and responding to COVID-19-related questions on time. ). It yields a more challenging table QA setting with two characteristics: models need to handle different types of tables. Homepage. implementations of preprocessing pipelines to re-generate the data for different configurations. Find datasets and benchmarks for question answering tasks, such as SQuAD, HotPotQA, TriviaQA, and more. Pandya, Brijesh S. 0 is a dataset containing open-ended questions about images. HotpotQA. This dataset is publicly available to the research community in the VLSP 2023 - ViVRC shared task challenge. SQuAD is a reading comprehension dataset, consisting of questions posed by crowd-workers on a set of Wikipedia articles, where the answer to every question is a segment of text from the corresponding question answer and visual question answer-ing - separately, covering the most representa-tive datasets, and then give some current chal-lenges of QA research. This dataset is created by the researchers at IBM and the University of California and can be viewed as the first large-scale dataset for QA over social media data. We developed 55 medical question-answer pairs across five different types of pain management: each question includes a detailed patient-specific medical scenario ("vignette") designed to enable the substitution of multiple different racial and gender "profiles" and to evaluate whether . , Multi-type) Jun 11, 2021 · Abstract. cz au hm dv dd sn um nk sv qo