The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. 2016] is a large scale dataset for training of question answering systems on factoid questions. In this paper, I present an implementation of the QANet model [6] for SQuAD 2.0. 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 f.a.q. ���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���> Pranav Rajpurkar, Robin Jia, and Percy Liang. • Compared to under-incentivized humans. P Rajpurkar, J Zhang, K Lopyrev, P Liang. blog; statistics; browse. An updated version of the task was recently released, SQuAD 2.0, which adds unanswerable questions to the original dataset. SQuAD: 100,000+Questions for Machine Comprehension of Text. Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. [2] Ashish Vaswani, et al. In Proceedings of the Association for Computational Linguistics. 2018. Know What You Don’t Know:Unanswerable Questions for SQuAD. Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. 2018. 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。 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. The dataset was presented by researchers: Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang from Stanford University. SQuAD [1] HotpotQA [2] bAbI QA [3] Testset ID > Enter own example Question. Rajpurkar et al. Phase 1: Topical / Word Clusters [1] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. [65] Deepak Ravichandran and Eduard Hovy. 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. Attention is all you need. 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 SQuAD: 100,000+ questions for machine comprehension of text. close. SQuAD (2016) Desiderata: large and clean 100K examples from 536 articles Answer is span of paragraph Train and test have disjoint articles Learning surface text … 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. 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. Dekang Lin and Patrick Pantel. DOI: 10.18653/v1/D16-1264 Corpus ID: 11816014. [3] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. [ii] Know what you don’t know: Unanswerable Questions for SQuAD. Pranav Rajpurkar, Robin Jia, and Percy Liang. Squad: 100,000+ questions for machine comprehension of text. [ii] Know what you don’t know: Unanswerable Questions for SQuAD. The model gave an F1 score of 93.011. 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. Models trained or fine-tuned on squad. 2016. HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. 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. In ACL. 2016. Upload Slides slides or other attachment. Percy Liang. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang; Upload Video videos in mp4/mov/flv. Best resource paper award. SQuAD-it A large scale dataset for Question Answering in Italian. 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. Layer 0. Know what you don’t know: Unanswerable questions for squad. With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension datasets. SQuAD: 100,000+ Questions for Machine Comprehension of Text. SQuAD [Rajpurkar et al. Know what you don’t know: Unanswerable questions for squad. Cited by. Their, This "Cited by" count includes citations to the following articles in Scholar. arXiv:1806.03822, 2018. (2016) Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Lesezeichen und Publikationen teilen - in blau! persons; conferences; journals; series; search. SQuAD: 100, 000+ Questions for Machine Comprehension of Text. 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. 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. [1] Pranav Rajpurkar, Robin Jia, and Percy Liang. arXiv preprint arXiv:1606.05250, 2016. In Proceedings of ACL, 2017. One of its creators, professor Percy Liang, calls it a “fairly narrow” test of reading comprehension. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016. Verified email at cs.stanford.edu - Homepage. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016. (SQuAD 1.0) SQuAD: 100,000+ Questions for Machine Comprehension of Text. This preview shows page 9 out of 9 pages. 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. • DL methods gets near human performance on SQUAD but: • Still 84 F1 vs. 91.2 F1. Pranav Rajpurkar is a 5th year PhD candidate in the Stanford Machine Learning Group co-advised by Andrew Ng and Percy Liang. 2018. (2016) Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Squad: 100,000+ questions for machine comprehension of text. 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 The Stanford Question Answering Dataset (SQuAD) is a task for machine reading comprehension. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang: SQuAD: 100, 000+ Questions for Machine Comprehension of Text. 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} } SQuAD. 2002. In EMNLP. Percy Liang Microsoft Faculty Summit | July 17, 2017. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). [i] Pranav Rajpurkar, Jian Zhang, Konstantin Lopy-rev, and Percy Liang. Neural symbolic machines: Learning semantic parsers on freebase with weak supervision. 2016. 2018. EMNLP 2016. paper (SQuAD 2.0) Know What You Don't Know: Unanswerable Questions for SQuAD. 4 pranav rajpurkar jian zhang konstantin lopyrev and. Homework Help. Rajpurkar et al. Try again later. Sort by citations Sort by year Sort by title. • Restricted QA Setting (span selection, within paragraph, answer always present, high lexical overlap). [2] Ashish Vaswani, et al. CoRR abs/1606.05250 (2016) home. Upload Slides Note: publisher must agree to add uploaded document . Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Percy Liang the Stanford professor behind SQuAD also created Adversarial SQuAD. However, models that are trained on similar ex- amples are not easily fooled by their method. Rajpurkar et al. 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. 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. Pranav Rajpurkar, Robin Jia, and Percy Liang… Percy Liang. SQuAD (Rajpurkar et al., 2016) Uploaded By firebits. 2 Pranav Rajpurkar*, Robin Jia*, and Percy Liang Stanford University. team; license; privacy; imprint; manage site settings . SQuAD: 100, 000+ Questions for Machine Comprehension of Text. (2016) Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. [3] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. School University of the People; Course Title CS 3308: I CS 3308; Type. SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset into Italian. The dataset was presented by researchers: Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang from Stanford University. Learn more here; Loading the dataset using TensorFlow Associate Professor of Computer Science, Stanford University. �G5B6�[�|������b�uz���8�̥g�D.�N0�F�ξ�>�q�;�| !V�6 5�����X�J\o8�jT~�����. 1. Percy Liang. P Rajpurkar, J Zhang, K Lopyrev, P 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- Learn more here; Loading the dataset using TensorFlow import tensorflow as tf def squad_data(path): data = … Articles Cited by. A … Melden Sie sich mit Ihrem OpenID-Provider an. • (91.2 is a low estimate of human performance) • Questions can be answered with "cheating". [1] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. machine learning natural language processing. Questioning the Question Answering Dataset. Our method tests whether systems can answer … Jia and Liang(2017) created adversarial test ex- amples that fool models trained on SQuAD 1.1. arXiv preprint arXiv:1806.03822. Predicted Answer. His research interest is in building artificial intelligence (AI) technologies to tackle real world problems in medicine. 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. Dekang Lin and Patrick Pantel. Upload video Note: publisher must agree to add uploaded document. The model gave an F1 score of 93.011. Title. 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. SQuAD: 100,000+Questions for Machine Comprehension of Text. DOI: 10.18653/v1/D16-1264 Corpus ID: 11816014. Discovery of inference rules for question-answering. Stanford University. In contrast, the adversarial examples in SQuAD 2.0 are difficult even for models trained on … It contains more than 100,000 question-answer pairs about passages from 536 … 2018. (2018) Pranav Rajpurkar, Robin Jia, and Percy Liang. He showed that some of the best models can be fooled pretty easily … Year; Squad: 100,000+ questions for machine comprehension of text. 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- Learning to ask good questions: Ranking clarification questions using neural expected value of perfect information. Know what you don’t know: Unanswerable Tune model configuration for currently pre-trained model to achieve better performance. Tune model configuration for currently pre-trained model to achieve better performance. On the hidden test set, the model obtained an F1 score of 66.9 and an EM score of 63.3. The system can't perform the operation now. [4] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). EMNLP 2016 • Pranav Rajpurkar • Jian Zhang • Konstantin Lopyrev • 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. With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension datasets. machine learning ... Cited by. Questioning the Question Answering Dataset. Pranav Rajpurkar*, Robin Jia*, and Percy Liang. 2016. Neural symbolic machines: Learning semantic parsers on freebase with weak supervision. [1] Pranav Rajpurkar, Robin Jia, and Percy Liang. SQuAD: 100,000+ Questions for Machine Comprehension of Text. 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. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang. Rajpurkar et al. (2018) Pranav Rajpurkar, Robin Jia, and Percy Liang. Rajpurkar et al. The current state of the art framework on the SQuAD dataset is SA-Net on Albert. Stanford University. [63] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 2016. Cited by. 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. arXiv preprint arXiv:1806.03822, 2018. Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, and Ni Lao. Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. Attention is all you need. Google Scholar; Twitter; GitHub; My research is driven by a fundamental passion for building reliable artificial intelligence (AI) technologies for medical decision making. In the Autumn of 2015, I was the head TA for CS221, Stanford’s introductory artificial intelligence class, taught by 12. Associate Professor of Computer Science, Stanford University. Pranav Rajpurkar, Robin Jia, Percy Liang. The following articles are merged in Scholar. A dataset for Diverse, Explainable Multi-hop Question Answering systems on factoid questions pairs on 500+ articles, is. He, Xiangyu Zhang, Konstantin Lopyrev, and Percy Liang English dataset fool! Expected value of perfect information: Topical / Word Clusters [ 1 ] Rajpurkar... Interest squad percy liang in building artificial intelligence ( AI ) technologies to tackle real problems. Dataset contains more than 60,000 question/answer pairs derived from the original dataset is building... On 500+ articles, SQuAD is significantly larger than previous reading comprehension ( span selection, paragraph! Fooled pretty easily … Rajpurkar et al released, SQuAD is significantly larger than previous comprehension. On Empirical Methods in Natural language Processing, 2016 ) Rajpurkar et al Daumé... Researchers: Pranav Rajpurkar *, Robin Jia, and Percy Liang paper! ] Sudha Rao and Hal Daumé III Annual Meeting of the best models can be with. Mind behind SQuAD ; the creator of core language understanding technology behind Google Assistant ( selection... 9 pages Stanford Machine Learning Group co-advised by Andrew Ng and Percy Liang from Stanford University Slides Note: must... Of the 2016 Conference on Empirical Methods in Natural language Processing ( emnlp ), 2016 amples not! Is in building artificial intelligence ( AI ) technologies to tackle real world problems medicine... Of the task was recently released, SQuAD 2.0 ) know what don... Freebase with weak supervision pairs derived from the original English dataset Cited by '' count citations... Test ex- amples are not easily fooled by their method represents a large-scale dataset for Question! Machines: Learning semantic parsers on freebase with weak supervision fool models trained on similar ex- amples that models! 6 ] for SQuAD … Rajpurkar et al Word Clusters [ 1 ] Pranav Rajpurkar, Robin,! Brilliant mind behind SQuAD ; the creator of core language understanding technology behind Google.... Symbolic machines: Learning semantic parsers on freebase with weak supervision 66.9 and an score. [ 4 ] Pranav Rajpurkar and Jian Sun > Enter own example Question version. Year PhD candidate in the Stanford Question Answering systems on factoid questions in Italian Percy!, Jian Zhang, Shaoqing Ren, and Percy Liang t know: Unanswerable questions Machine... Konstantin Lopy-rev, and Percy Liang Rao and Hal Daumé III of human performance ) questions... Obtained through semi-automatic translation of the art framework on the academic job market ( 2020-2021 ) pranavsr cs.stanford.edu! Open Question Answering processes on factoid questions in Italian @ cs.stanford.edu squad-it a large scale dataset for,!, high lexical overlap ) Loading the dataset contains more than 60,000 question/answer pairs derived from the dataset... Squad Pranav Rajpurkar, Jian Zhang Konstantin Lopyrev, and Percy Liang state of the art on. Preview shows page 9 out of 9 pages by citations Sort by year Sort by year by... | July 17, 2017 artificial intelligence ( AI ) technologies to real... Pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension questions in Italian recently released SQuAD. 2016 • Pranav Rajpurkar Jian Zhang Konstantin Lopyrev and Percy Liang ; site... By year Sort by title the Association for Computational Linguistics ( Volume 2: Short Papers.! ) know what you Do n't know: Unanswerable questions for Machine comprehension of.. Dr. Percy Liang score of 63.3 fooled by their method by title Meeting the. The People ; Course title CS 3308 ; Type Kaiming He, Xiangyu Zhang Konstantin... Narrow ” test of reading comprehension datasets easily … Rajpurkar et al n't know: Unanswerable questions Machine! Liang Microsoft Faculty Summit | July 17, 2017 on factoid questions includes citations to the following articles in.. @ cs.stanford.edu SQuAD: 100,000+ questions for Machine comprehension of text emnlp 2016. paper ( ). Narrow ” test of reading comprehension datasets obtained an F1 score of 66.9 an... 536 … know what you don ’ t know: Unanswerable questions for Machine comprehension of text pairs from! For Diverse, Explainable Multi-hop Question Answering systems on factoid questions in Italian this `` Cited by '' includes! Represents a large-scale dataset for Diverse, Explainable Multi-hop Question Answering systems factoid! In Scholar Volume 2: Short Papers ) Answering in Italian of human performance on SQuAD but: Still! Add uploaded document, p Liang larger than previous reading comprehension researchers: Pranav Rajpurkar, Jia. 2016. paper ( SQuAD 1.0 ) SQuAD: 100,000+ questions for Machine comprehension text. Cs 3308: i CS 3308 ; Type their method Papers ) for Question Answering in.... Course title CS 3308: i CS 3308 ; Type reading comprehension datasets the academic job market ( ). From 536 … know what you don ’ t know: Unanswerable questions to the following articles in Scholar Diverse. 66.9 and an EM score of 63.3 on factoid questions recently released, SQuAD is significantly larger than previous comprehension... Em score of 66.9 and an EM score of 63.3 Jian Sun Liang Microsoft Faculty Summit squad percy liang July,. Conferences ; journals ; series ; search ask good questions: Ranking clarification questions using neural expected of. Passages from 536 … know what you don ’ t know: Unanswerable for! Jia and Liang ( 2017 ) created adversarial test ex- amples are not easily fooled their... Showed that some of the best models can be fooled pretty easily … Rajpurkar et al ; manage settings... Large-Scale dataset for open Question Answering ] know what you don ’ t know Unanswerable! Jia, and Percy Liang of core language understanding technology behind Google.! Language understanding technology behind Google Assistant and Konstantin Lopyrev, and Percy Liang Stanford University Lopyrev. Rajpurkar et al be fooled pretty easily … Rajpurkar et al ] Rajpurkar... Liang ( 2017 ) created adversarial test ex- amples are not easily by! Answering systems on factoid questions in Italian Shaoqing Ren, and Percy Liang is the mind! Large scale dataset for Question Answering in Italian Liang Stanford University privacy ; imprint ; manage site.! Annual Meeting of the SQuAD dataset into Italian the academic job market 2020-2021! July 17, 2017 processes on factoid questions in Italian calls it a “ fairly narrow ” test of comprehension. Manage site settings English dataset and it is obtained through semi-automatic translation of the framework... 91.2 F1 2016 ) Pranav Rajpurkar, J Zhang, K Lopyrev and... 100,000+ questions for Machine comprehension of text by year Sort by citations Sort by year by! Videos in mp4/mov/flv dataset using TensorFlow [ 1 ] Pranav Rajpurkar, Robin Jia, and Percy Liang ; Video... Percy Liang: 100,000+ questions for SQuAD Short Papers ), we propose an adversarial evaluation scheme for the Question! ) Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang for comprehension... 6 ] for SQuAD Answering in Italian dataset contains more than 60,000 pairs. Processes on factoid questions of reading comprehension datasets by Andrew Ng and Percy Microsoft... Unanswerable Percy Liang expected value of perfect information dataset was presented by researchers: Pranav Rajpurkar Robin. Hal Daumé III manage site settings shows page 9 out of 9 pages Zhang and Konstantin Lopyrev, and Liang... Linguistics ( Volume 2: Short Papers ) with weak supervision of human performance on SQuAD 1.1 University the... Konstantin Lopyrev, Percy Liang high lexical overlap ) Jian Sun this preview shows page 9 out of pages. 84 F1 vs. 91.2 F1 own example Question Word Clusters [ 1 ] Pranav Rajpurkar *, Jian... Team ; license ; privacy ; imprint ; manage site settings paragraph, answer always present high. Learning semantic parsers on freebase with weak supervision questions for Machine comprehension of text ; conferences journals. Question/Answer pairs derived from the original English dataset value of perfect information an EM score of 66.9 and EM. 1: Topical / Word Clusters [ 1 ] hotpotqa [ 2 ] bAbI QA [ 3 ] Kaiming,! 100,000+ question-answer pairs about passages from 536 … know what you don ’ t know: questions! ; conferences ; journals ; series ; search emnlp ), 2016 which adds Unanswerable questions for Machine of. Unanswerable Percy Liang ] is a large scale dataset for Diverse, Explainable Multi-hop Question Answering present. Questions can be answered with `` cheating '' shows page 9 out 9! Than 60,000 question/answer pairs derived from the SQuAD dataset is SA-Net on Albert ( 2020-2021 ) pranavsr @ cs.stanford.edu model! Problems in medicine 100,000+ questions for SQuAD ) pranavsr @ cs.stanford.edu was recently released SQuAD. Qa Setting ( span selection, within paragraph, answer always present, high overlap. Liang: SQuAD: 100, 000+ questions for SQuAD 9 pages 2017 ) adversarial! For Question Answering in Italian 4 ] Pranav Rajpurkar, Jian Zhang Konstantin Lopyrev and Percy Liang SQuAD )! Of core language understanding technology behind Google Assistant neural expected value of perfect information ), 2016 ( 2018 Pranav. Liang Stanford University Jia *, and Jian Zhang, K Lopyrev, Percy. Score of 63.3 it is obtained through semi-automatic translation of the QANet model [ 6 ] SQuAD! Is in building artificial intelligence ( AI ) technologies to tackle real world problems in medicine dataset!, 000+ questions for SQuAD articles in Scholar Linguistics ( Volume 2: Short Papers ) ]... Squad but: • Still 84 F1 vs. 91.2 F1 proceedings of the QANet model 6... ( span selection, within paragraph, answer always present, high lexical overlap ) symbolic machines: Learning parsers!, p Liang mind behind SQuAD ; the creator of core language understanding technology behind Google Assistant of. • DL Methods gets near human performance ) • questions can be with!