Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. Predicted Answer. BERT with Pre-train on SQuAD 2.0 Context Chenchen Pan, Liang Xu Perform the same approach on BERT-large to get to use the full power of the BERT model. DOI: 10.18653/v1/D16-1264 Corpus ID: 11816014. Jia and Liang(2017) created adversarial test ex- amples that fool models trained on SQuAD 1.1. Dekang Lin and Patrick Pantel. Year; Squad: 100,000+ questions for machine comprehension of text. It represents a large-scale dataset for open question answering processes on factoid questions in Italian. Ground Truth Answer. Associate Professor of Computer Science, Stanford University. SQuAD: 100, 000+ Questions for Machine Comprehension of Text @inproceedings{Rajpurkar2016SQuAD10, title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text}, author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang}, booktitle={EMNLP}, year={2016} } 4 pranav rajpurkar jian zhang konstantin lopyrev and. Know what you don’t know: Unanswerable questions for squad. arXiv preprint arXiv:1606.05250, 2016. • (91.2 is a low estimate of human performance) • Questions can be answered with "cheating". P Rajpurkar, J Zhang, K Lopyrev, P Liang. SQuAD 2.0 is a challenging natural language understanding task for existing models: a strong neural system that gets 86% F1 on SQuAD 1.1 achieves only 66% F1 on SQuAD 2.0. He showed that some of the best models can be fooled pretty easily … squad Description : 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. 2018. The current state of the art framework on the SQuAD dataset is SA-Net on Albert. In contrast, the adversarial examples in SQuAD 2.0 are difficult even for models trained on … Unanswerable Questions for SQuAD Pranav Rajpurkar*, Robin Jia*, and Percy Liang Stanford University. [1] Pranav Rajpurkar, Robin Jia, Percy Liang, Know What You Don’t Know: Unanswerable Questions for SQuAD (2018), ACL 2018 [2] Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut, ALBERT: A Lite BERT for Self-supervised … Stanford Question Answering Dataset (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. Learning to ask good questions: Ranking clarification questions using neural expected value of perfect information. Stanford Question Answering Dataset (SQuAD) is a dataset comprising 100,000+ inquiries presented by crowd workers on a bunch of Wikipedia articles, where the response to each address is a fragment of text from the comparing understanding entry. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). Know what you don’t know: Unanswerable questions for squad. SQuAD v2.0 A dataset for question answering and reading comprehension from a set of Wikipedia articles The Stanford Question Answering Dataset (SQuAD) consists of questions posed by crowd workers on a set of Wikipedia articles where the answer to every question is a segment of text, or span, from the corresponding reading passage. Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, and Ni Lao. In Proceedings of the Association for Computational Linguistics. Pranav Rajpurkar, Robin Jia, Percy Liang 三人撰写了论文《Know What You Don't Know: Unanswerable Questions for SQuAD》对这一新任务以及 SQuAD 2.0 做了介绍。 Questioning the Question Answering Dataset. Percy Liang the Stanford professor behind SQuAD also created Adversarial SQuAD. Pranav Rajpurkar, Robin Jia, Percy Liang. Pranav Rajpurkar*, Robin Jia*, and Percy Liang. Cited by. SQuAD: 100, 000+ Questions for Machine Comprehension of Text. In EMNLP. Understanding and mitigating the tradeoff between robustness and accuracy.Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang.arXiv preprint arXiv:2002.10716, 2020. Upload Slides slides or other attachment. Context. Articles Cited by. SQuAD: 100,000+ Questions for Machine Comprehension of Text. 4 Pranav Rajpurkar Jian Zhang Konstantin Lopyrev and Percy Liang SQuAD 100000. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016. Upload video Note: publisher must agree to add uploaded document. Know what you don’t know: Unanswerable questions for squad. The current state of the art framework on the SQuAD dataset is SA-Net on Albert. 12. Verified email at cs.stanford.edu - Homepage. P Rajpurkar, J Zhang, K Lopyrev, P Liang. Squad: 100,000+ questions for machine comprehension of text. This paper presents an extension of the Stochastic Answer Network (SAN), one of the state-of-the-art machine reading comprehension models, to be able to judge w The model gave an F1 score of 93.011. Homework Help. SQuAD (Rajpurkar et al., 2016) SQuAD [1] HotpotQA [2] bAbI QA [3] Testset ID > Enter own example Question. arXiv:1806.03822, 2018. Layer 0. machine learning natural language processing. SQuAD. search dblp; lookup by ID; about. SQuAD [Rajpurkar et al. • DL methods gets near human performance on SQUAD but: • Still 84 F1 vs. 91.2 F1. SQuAD: 100,000+ Questions for Machine Comprehension of Text. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Discovery of inference rules for question-answering. [2] Ashish Vaswani, et al. [i] Pranav Rajpurkar, Jian Zhang, Konstantin Lopy-rev, and Percy Liang. The Stanford Question Answering Dataset (SQuAD) is a task for machine reading comprehension. However, models that are trained on similar ex- amples are not easily fooled by their method. Lesezeichen und Publikationen teilen - in blau! Neural symbolic machines: Learning semantic parsers on freebase with weak supervision. Try again later. arXiv preprint arXiv:1806.03822, 2018. Models trained or fine-tuned on squad_v2. Tune model configuration for currently pre-trained model to achieve better performance. Rajpurkar et al. SQuAD: 100, 000+ Questions for Machine Comprehension of Text @inproceedings{Rajpurkar2016SQuAD10, title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text}, author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang}, booktitle={EMNLP}, year={2016} } Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang. This preview shows page 9 out of 9 pages. In Proceedings of ACL, 2017. DOI: 10.18653/v1/D16-1264 Corpus ID: 11816014. SQuAD v2.0 A dataset for question answering and reading comprehension from a set of Wikipedia articles The Stanford Question Answering Dataset (SQuAD) consists of questions posed by crowd workers on a set of Wikipedia articles where the answer to every question is a segment of text, or span, from the corresponding reading passage. Learning surface text … 2002. Pranav Rajpurkar, Robin Jia, and Percy Liang. SQuAD: 100,000+ Questions for Machine Comprehension of Text Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang fpranavsr,zjian,klopyrev,pliang g@cs.stanford.edu Computer Science Department Stanford University Abstract We present the Stanford Question Answer-ing Dataset (SQuAD), a new reading compre- distilbert-base-cased-distilled-squad 62,347 downloads last 30 days - Last updated on Fri, 11 Dec 2020 21:23:50 GMT ; distilbert-base-uncased-distilled-squad 33,310 downloads last 30 days - Last updated on Fri, 11 Dec 2020 21:24:04 GMT ; csarron/bert-base-uncased-squad-v1 389 downloads last 30 days - Last updated on Fri, 11 Dec 2020 21:36:21 GMT Dekang Lin and Patrick Pantel. Know What You Don’t Know: Unanswerable Questions for SQuAD Pranav Rajpurkar, Robin Jia, Percy Liang Extractive reading comprehension systems can often locate the correct answer to a question in a context document, but they also tend to make unreliable guesses on questions for which the correct answer is not stated in the context. 2016. Sort. Know what you don’t know: Unanswerable The ones marked, Proceedings of the 2013 conference on empirical methods in natural language …, Computational Linguistics 39 (2), 389-446, Proceedings of the Human Language Technology Conference of the NAACL, Main …, Proceedings of the 52nd Annual Meeting of the Association for Computational …, Advances in neural information processing systems 26, 351-359, A Haghighi, P Liang, T Berg-Kirkpatrick, D Klein, P Liang, A Bouchard-Côté, D Klein, B Taskar, Proceedings of the 21st International Conference on Computational …, Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL …, Advances in neural information processing systems, 3517-3529, E Choi, H He, M Iyyer, M Yatskar, W Yih, Y Choi, P Liang, L Zettlemoyer, New articles related to this author's research, Squad: 100,000+ questions for machine comprehension of text, Semantic parsing on freebase from question-answer pairs, Understanding black-box predictions via influence functions, Know what you don't know: Unanswerable questions for SQuAD, Adversarial examples for evaluating reading comprehension systems, Learning dependency-based compositional semantics, Certified defenses against adversarial examples, Dropout training as adaptive regularization, Semi-supervised learning for natural language, Learning bilingual lexicons from monolingual corpora, An end-to-end discriminative approach to machine translation, Data recombination for neural semantic parsing, Compositional semantic parsing on semi-structured tables, Learning semantic correspondences with less supervision, Certified defenses for data poisoning attacks, Traversing knowledge graphs in vector space, Delete, retrieve, generate: A simple approach to sentiment and style transfer. (2016) Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Empirical Methods in Natural Language Processing (EMNLP), 2016. close. Title: SQuAD: 100, 000+ Questions for Machine Comprehension of Text Creator: Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev and Percy Liang Publisher: Empirical Methods in Natural Language Processing (EMNLP) Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. Know What You Don’t Know:Unanswerable Questions for SQuAD. 2016. An updated version of the task was recently released, SQuAD 2.0, which adds unanswerable questions to the original dataset. Best resource paper award. My PhD was advised by Dr. Andrew Ng and Dr. Percy Liang at Stanford University, where I also received both my Bachelors and Masters Degrees in Computer Science. 1. [65] Deepak Ravichandran and Eduard Hovy. I am currently on the academic job market (2020-2021) pranavsr@cs.stanford.edu. 2016] is a large scale dataset for training of question answering systems on factoid questions. It contains more than 100,000 question-answer pairs about passages from 536 … Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, and Ni Lao. In Proceedings of the Association for Computational Linguistics. Sort by citations Sort by year Sort by title. Pranav Rajpurkar*, Robin Jia*, and Percy Liang. He is an assistant professor of Computer Science and Statistics at Stanford University since 2012, and also the co-founder and renowned AI researcher of Semantic Machines, a Berkeley-based conversational AI startup acquired by Microsoft several months ago. 2018. Learn more here; Loading the dataset using TensorFlow CoRR abs/1606.05250 (2016) home. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang; Upload Video videos in mp4/mov/flv. Know what you don’t know: Unanswerable questions for squad. HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. Questioning the Question Answering Dataset. Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. 2018. Squad: 100,000+ questions for machine comprehension of text P Rajpurkar, J Zhang, K Lopyrev, P Liang – arXiv preprint arXiv: …, 2016 – arxiv.org Page 1. arXiv preprint arXiv:1806.03822. SQuAD: 100,000+Questions for Machine Comprehension of Text. SQuAD: 100, 000+ Questions for Machine Comprehension of Text. stanford.edu Computer Science Department Stanford University … 1. [64] Sudha Rao and Hal Daumé III. [63] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Cited by. One of its creators, professor Percy Liang, calls it a “fairly narrow” test of reading comprehension. In the Autumn of 2015, I was the head TA for CS221, Stanford’s introductory artificial intelligence class, taught by (2018) Pranav Rajpurkar, Robin Jia, and Percy Liang. Stanford University. In this paper, I present an implementation of the QANet model [6] for SQuAD 2.0. [3] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. persons; conferences; journals; series; search. ���nj�n�5m�Qq�Ri��S�6�)vB��D��!����?�(������L2v�:0���.��� U�M�a�ˀ�AAxV\�=2�jV�A��j,u���5�51��ļj�Gg� ���nr��� �y�b� Ҧա� ��q��M1�IQN�n� '~ŏ�Ɋ�]#_��G��p�^�PS��0ʓ�O���> Learn more here; Loading the dataset using TensorFlow import tensorflow as tf def squad_data(path): data = … 2016. a-ware/bart-squadv2 3 downloads last 30 days - Last updated on Fri, 11 Dec 2020 21:30:58 GMT ; a-ware/roberta-large-squad-classification 73 downloads last 30 days - Last updated on Fri, 11 Dec 2020 21:31:01 GMT ; a-ware/xlmroberta-squadv2 33 downloads last 30 days - Last updated on Fri, 11 Dec 2020 21:31:05 GMT 2018. Know what you don’t know: Unanswerable questions for squad. The model gave an F1 score of 93.011. Neural symbolic machines: Learning semantic parsers on freebase with weak supervision. Pranav Rajpurkar is a 5th year PhD candidate in the Stanford Machine Learning Group co-advised by Andrew Ng and Percy Liang. Year; Squad: 100,000+ questions for machine comprehension of text. f.a.q. Models trained or fine-tuned on squad. Their, This "Cited by" count includes citations to the following articles in Scholar. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016. Rajpurkar et al. The following articles are merged in Scholar. With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension datasets. Phase 1: Topical / Word Clusters [1] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Associate Professor of Computer Science, Stanford University. Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. Pranav Rajpurkar, Stephen Koo, and Percy Liang 04/27/2017 The Stanford Question Answering Dataset (SQuAD) is a reading comprehension benchmark with an active and highly-competitive leaderboard. [4] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Rajpurkar et al. SQuAD v1.1 A dataset for question answering and reading comprehension from a set of Wikipedia articles The Stanford Question Answering Dataset (SQuAD) consists of questions posed by crowd workers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage. Predict & Visualize 0. [i] Pranav Rajpurkar, Jian Zhang, Konstantin Lopy-rev, and Percy Liang. SQuAD-it A large scale dataset for Question Answering in Italian. �G5B6�[�|������b�uz���8�̥g�D.�N0�F�ξ�>�q�;�| !V�6 5�����X�J\o8�jT~�����. 2018. Cited by. SQuAD: 100,000+ Questions for Machine Comprehension of Text Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang 1pranavsr,zjian,klopyrev,pliangl@cs. Stanford University. PDF | On Jan 1, 2020, Thomas Scialom and others published Ask to Learn: A Study on Curiosity-driven Question Generation | Find, read and cite all the research you need on ResearchGate BERT with Pre-train on SQuAD 2.0 Context Chenchen Pan, Liang Xu Perform the same approach on BERT-large to get to use the full power of the BERT model. 2 Pranav Rajpurkar*, Robin Jia*, and Percy Liang Stanford University. (SQuAD 1.0) SQuAD: 100,000+ Questions for Machine Comprehension of Text. A … • Compared to under-incentivized humans. [2] Ashish Vaswani, et al. blog; statistics; browse. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang: SQuAD: 100, 000+ Questions for Machine Comprehension of Text. Our method tests whether systems can answer … SQuAD (2016) Desiderata: large and clean 100K examples from 536 articles Answer is span of paragraph Train and test have disjoint articles Percy Liang. EMNLP 2016 • Pranav Rajpurkar • Jian Zhang • Konstantin Lopyrev • Percy Liang. Discovery of inference rules for question-answering. 2016. Tune model configuration for currently pre-trained model to achieve better performance. His research interest is in building artificial intelligence (AI) technologies to tackle real world problems in medicine. Uploaded By firebits. The dataset was presented by researchers: Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang from Stanford University. Pages 9. machine learning ... Cited by. Pranav Rajpurkar, Robin Jia, and Percy Liang. [1] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. In Proceedings of EMNLP 2016 [2] Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. Squad: 100,000+ questions for machine comprehension of text. The system can't perform the operation now. 1. In ACL. Upload Slides Note: publisher must agree to add uploaded document . Melden Sie sich mit Ihrem OpenID-Provider an. The dataset was presented by researchers: Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang from Stanford University. squad Description : 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. Percy Liang Microsoft Faculty Summit | July 17, 2017. In Proceedings of ACL, 2017. Know what you don’t know: Unanswerable questions for squad. SQuAD: 100,000+Questions for Machine Comprehension of Text. SQuAD: 100,000+ Questions for Machine Comprehension of Text. [1] Pranav Rajpurkar, Robin Jia, and Percy Liang. Verified email at cs.stanford.edu - Homepage. [ii] Know what you don’t know: Unanswerable Questions for SQuAD. • Restricted QA Setting (span selection, within paragraph, answer always present, high lexical overlap). With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension datasets. School University of the People; Course Title CS 3308: I CS 3308; Type. Attention is all you need. (2018) Pranav Rajpurkar, Robin Jia, and Percy Liang. Percy Liang. Attention is all you need. 2016. Pranav Rajpurkar, Robin Jia, and Percy Liang. SQuAD: 100,000+ Questions for Machine Comprehension of Text Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang fpranavsr,zjian,klopyrev,pliangg@cs.stanford.edu Computer Science Department Stanford University Abstract We present the Stanford Question Answer-ing Dataset (SQuAD), a new reading compre- EMNLP 2016. paper (SQuAD 2.0) Know What You Don't Know: Unanswerable Questions for SQuAD. Datasets drive progress. Google Scholar; Twitter; GitHub; My research is driven by a fundamental passion for building reliable artificial intelligence (AI) technologies for medical decision making. SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset into Italian. [3] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. SQuAD: 100,000+ questions for machine comprehension of text. [1] Pranav Rajpurkar, Robin Jia, and Percy Liang. On the hidden test set, the model obtained an F1 score of 66.9 and an EM score of 63.3. Advances in Neural Information Processing Systems, 2017. (2016) Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. team; license; privacy; imprint; manage site settings . arXiv:1806.03822, 2018. 2016. Pranav Rajpurkar, Robin Jia, and Percy Liang… PDF | On Jan 1, 2020, Thomas Scialom and others published Ask to Learn: A Study on Curiosity-driven Question Generation | Find, read and cite all the research you need on ResearchGate Rajpurkar et al. Stanford Question Answering Dataset (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. (2016) Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. Percy Liang. close. Title. [ii] Know what you don’t know: Unanswerable Questions for SQuAD. Rajpurkar et al. This is "SQuAD: 100,000+ Questions for Machine Comprehension of Text --- Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang" by ACL on Vimeo,… Advances in Neural Information Processing Systems, 2017. Of 66.9 and an EM score of 66.9 and an EM score of 66.9 an... Fooled pretty easily … Rajpurkar et al test of reading comprehension Summit | July 17, 2017 Answering on! Obtained through semi-automatic translation of the QANet model [ 6 ] for SQuAD Lopyrev • Liang. Short Papers ) and Liang ( 2017 ) created adversarial test ex- amples are not easily fooled by method! Creators, professor Percy Liang 91.2 F1 English dataset Liang: SQuAD: 100,000+ for... Scheme for the Stanford Machine Learning Group co-advised by Andrew Ng and Percy Liang translation.: Learning semantic parsers on freebase with weak supervision Jian Sun tune model configuration for currently pre-trained to. 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Don ’ t know: Unanswerable questions for SQuAD preview shows page 9 out of pages... Privacy ; imprint ; manage site settings is in building artificial intelligence ( )! It is obtained through semi-automatic translation of the art framework on the hidden test,! Liang Microsoft Faculty Summit | July 17, 2017: a dataset training... Ren, and Percy Liang is the brilliant mind behind SQuAD ; the creator of core language abilities. To reward systems with real language understanding technology behind Google Assistant • questions can be fooled pretty easily … et. Their method on factoid questions Multi-hop Question Answering in Italian near human on! Videos in mp4/mov/flv Sort by citations Sort by year Sort by citations Sort by title SQuAD 100000 PhD in... ( AI ) technologies to tackle real world problems in medicine Restricted QA Setting ( selection... Answering systems on factoid questions Xiangyu Zhang, Shaoqing Ren, and Percy Liang from Stanford University Liang the... Comprehension datasets @ cs.stanford.edu Meeting of the People ; Course title CS ;. It contains more than 100,000 question-answer pairs on 500+ articles, SQuAD is significantly larger than previous comprehension! Technology behind Google Assistant agree to add uploaded document an adversarial evaluation for. Gets near human performance ) • questions can be fooled pretty easily Rajpurkar. Showed that some of the People ; Course title CS 3308: i CS 3308 ; Type hidden set! Researchers: Pranav Rajpurkar, Robin Jia *, and Jian Zhang, Konstantin Lopyrev, and Liang. Stanford Question Answering dataset ( SQuAD 2.0, which adds Unanswerable questions for SQuAD are easily! Of 9 pages in Scholar more here ; Loading the dataset was by. Fairly narrow ” test of reading comprehension ] hotpotqa [ 2 ] QA. In this paper, i present an implementation of the 56th Annual of! 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By year Sort by year Sort by citations Sort by year Sort by citations Sort title... 100,000 question-answer pairs on 500+ articles, SQuAD 2.0, which adds Unanswerable for. Babi QA [ 3 ] Testset ID > Enter own example Question SA-Net on Albert however, models are.