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SQuAD. HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering. Advances in Neural Information Processing Systems, 2017. 1. SQuAD-it A large scale dataset for Question Answering in Italian. CoRR abs/1606.05250 (2016) home. SQuAD (Rajpurkar et al., 2016) Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016. close. SQuAD: 100,000+ Questions for Machine Comprehension of Text. Phase 1: Topical / Word Clusters [1] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Their, This "Cited by" count includes citations to the following articles in Scholar. Pranav Rajpurkar, Robin Jia, and Percy Liang. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang. He showed that some of the best models can be fooled pretty easily … Empirical Methods in Natural Language Processing (EMNLP), 2016. Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. (2018) Pranav Rajpurkar, Robin Jia, and Percy Liang. Pranav Rajpurkar, Robin Jia, Percy Liang. Know what you don’t know: Unanswerable questions for squad. Rajpurkar et al. (2018) Pranav Rajpurkar, Robin Jia, and Percy Liang. The current state of the art framework on the SQuAD dataset is SA-Net on Albert. Try again later. 1. [1] Pranav Rajpurkar, Robin Jia, and Percy Liang. 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 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. 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 Know what you don’t know: Unanswerable questions for squad. �G5B6�[�|������b�uz���8�̥g�D.�N0�F�ξ�>�q�;�| !V�6 5�����X�J\o8�jT~�����. Rajpurkar et al. 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. Datasets drive progress. arXiv preprint arXiv:1606.05250, 2016. Percy Liang Microsoft Faculty Summit | July 17, 2017. The model gave an F1 score of 93.011. Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. Melden Sie sich mit Ihrem OpenID-Provider an. Pranav Rajpurkar*, Robin Jia*, and Percy Liang. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers). Google Scholar; Twitter; GitHub; My research is driven by a fundamental passion for building reliable artificial intelligence (AI) technologies for medical decision making. DOI: 10.18653/v1/D16-1264 Corpus ID: 11816014. Cited by. 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} } Context. In the Autumn of 2015, I was the head TA for CS221, Stanford’s introductory artificial intelligence class, taught by Stanford University. In contrast, the adversarial examples in SQuAD 2.0 are difficult even for models trained on … With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension datasets. Predicted Answer. Squad: 100,000+ questions for machine comprehension of text. 2016. machine learning ... Cited by. [65] Deepak Ravichandran and Eduard Hovy. This is "SQuAD: 100,000+ Questions for Machine Comprehension of Text --- Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang" by ACL on Vimeo,… ���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���> [3] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Pranav Rajpurkar, Robin Jia, and Percy Liang. Articles Cited by. 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. 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 [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 … arXiv preprint arXiv:1806.03822, 2018. 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. Attention is all you need. The model gave an F1 score of 93.011. Associate Professor of Computer Science, Stanford University. 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. 2016. 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. Unanswerable Questions for SQuAD Pranav Rajpurkar*, Robin Jia*, and Percy Liang Stanford University. 2018. Pranav Rajpurkar*, Robin Jia*, and Percy Liang. Know what you don’t know: Unanswerable questions for squad. Best resource paper award. Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, and Ni Lao. Cited by. Standard accuracy metrics indicate that reading comprehension systems are making rapid progress, but the extent to which these systems truly understand language remains unclear. In Proceedings of the Association for Computational Linguistics. Rajpurkar et al. In this paper, I present an implementation of the QANet model [6] for SQuAD 2.0. 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. Year; Squad: 100,000+ questions for machine comprehension of text. 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. [64] Sudha Rao and Hal Daumé III. machine learning natural language processing. Sort. [ii] Know what you don’t know: Unanswerable Questions for SQuAD. 1. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang; Upload Video videos in mp4/mov/flv. Sort by citations Sort by year Sort by title. Know What You Don’t Know:Unanswerable Questions for SQuAD. blog; statistics; browse. To reward systems with real language understanding abilities, we propose an adversarial evaluation scheme for the Stanford Question Answering Dataset (SQuAD). 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. Tune model configuration for currently pre-trained model to achieve better performance. [4] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. arXiv preprint arXiv:1806.03822. 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) Pranav Rajpurkar, Robin Jia, and Percy Liang. Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, Percy Liang: SQuAD: 100, 000+ Questions for Machine Comprehension of Text. 2 Pranav Rajpurkar*, Robin Jia*, and Percy Liang Stanford University. Neural symbolic machines: Learning semantic parsers on freebase with weak supervision. Homework Help. [1] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. 4 pranav rajpurkar jian zhang konstantin lopyrev and. Chen Liang, Jonathan Berant, Quoc Le, Kenneth D. Forbus, and Ni Lao. The dataset contains more than 60,000 question/answer pairs derived from the original English dataset. The system can't perform the operation now. SQuAD: 100,000+Questions for Machine Comprehension of Text. 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] is a large scale dataset for training of question answering systems on factoid questions. His research interest is in building artificial intelligence (AI) technologies to tackle real world problems in medicine. Stanford University. [2] Ashish Vaswani, et al. Squad: 100,000+ questions for machine comprehension of text. Title. 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. One of its creators, professor Percy Liang, calls it a “fairly narrow” test of reading comprehension. School University of the People; Course Title CS 3308: I CS 3308; Type. In Proceedings of EMNLP 2016 [2] Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, and Christopher D. Manning. SQuAD-it is derived from the SQuAD dataset and it is obtained through semi-automatic translation of the SQuAD dataset into Italian. DOI: 10.18653/v1/D16-1264 Corpus ID: 11816014. Pages 9. • DL methods gets near human performance on SQUAD but: • Still 84 F1 vs. 91.2 F1. Verified email at cs.stanford.edu - Homepage. Neural symbolic machines: Learning semantic parsers on freebase with weak supervision. An updated version of the task was recently released, SQuAD 2.0, which adds unanswerable questions to the original dataset. Rajpurkar et al. Pranav Rajpurkar is a 5th year PhD candidate in the Stanford Machine Learning Group co-advised by Andrew Ng and Percy Liang. This preview shows page 9 out of 9 pages. search dblp; lookup by ID; about. The dataset was presented by researchers: Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang from Stanford University. Discovery of inference rules for question-answering. 12. 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: 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- 2016. The following articles are merged in Scholar. A … stanford.edu Computer Science Department Stanford University … 2018. • (91.2 is a low estimate of human performance) • Questions can be answered with "cheating". SQuAD [Rajpurkar et al. 2016. P Rajpurkar, J Zhang, K Lopyrev, P Liang. 2018. 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} } Uploaded By firebits. (2016) Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. Tune model configuration for currently pre-trained model to achieve better performance. P Rajpurkar, J Zhang, K Lopyrev, P Liang. SQuAD: 100,000+Questions for Machine Comprehension of Text. 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. 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. On the hidden test set, the model obtained an F1 score of 66.9 and an EM score of 63.3. 4 Pranav Rajpurkar Jian Zhang Konstantin Lopyrev and Percy Liang SQuAD 100000. However, models that are trained on similar ex- amples are not easily fooled by their method. 2016. In EMNLP. Know what you don’t know: Unanswerable questions for squad. 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- 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 Associate Professor of Computer Science, Stanford University. [ii] Know what you don’t know: Unanswerable Questions for SQuAD. Rajpurkar et al. It represents a large-scale dataset for open question answering processes on factoid questions in Italian. 2018. Learn more here; Loading the dataset using TensorFlow import tensorflow as tf def squad_data(path): data = … The dataset was presented by researchers: Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang from Stanford University. In Proceedings of ACL, 2017. Questioning the Question Answering Dataset. SQuAD [1] HotpotQA [2] bAbI QA [3] Testset ID > Enter own example Question. f.a.q. 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 I am currently on the academic job market (2020-2021) pranavsr@cs.stanford.edu. persons; conferences; journals; series; search. 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. EMNLP 2016 • Pranav Rajpurkar • Jian Zhang • Konstantin Lopyrev • Percy Liang. 2016. close. Our method tests whether systems can answer … (SQuAD 1.0) SQuAD: 100,000+ Questions for Machine Comprehension of Text. Percy Liang. Percy Liang. (2016) Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. In ACL. The Stanford Question Answering Dataset (SQuAD) is a task for machine reading comprehension. Dr. Percy Liang is the brilliant mind behind SQuAD; the creator of core language understanding technology behind Google Assistant. Models trained or fine-tuned on squad. Dekang Lin and Patrick Pantel. [i] Pranav Rajpurkar, Jian Zhang, Konstantin Lopy-rev, and Percy Liang. [i] Pranav Rajpurkar, Jian Zhang, Konstantin Lopy-rev, and Percy Liang. In Proceedings of ACL, 2017. Know what you don’t know: Unanswerable questions for squad. Layer 0. 2002. 2018. [1] Pranav Rajpurkar, Robin Jia, and Percy Liang. (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. With 100,000+ question-answer pairs on 500+ articles, SQuAD is significantly larger than previous reading comprehension datasets. Learning surface text … Percy Liang. 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. • Restricted QA Setting (span selection, within paragraph, answer always present, high lexical overlap). arXiv:1806.03822, 2018. Attention is all you need. Percy Liang the Stanford professor behind SQuAD also created Adversarial SQuAD. Upload video Note: publisher must agree to add uploaded document. Year; Squad: 100,000+ questions for machine comprehension of text. SQuAD: 100,000+ Questions for Machine Comprehension of Text. Lesezeichen und Publikationen teilen - in blau! SQuAD (2016) Desiderata: large and clean 100K examples from 536 articles Answer is span of paragraph Train and test have disjoint articles Jia and Liang(2017) created adversarial test ex- amples that fool models trained on SQuAD 1.1. Questioning the Question Answering Dataset. Know what you don’t know: Unanswerable questions for squad. SQuAD: 100,000+ Questions for Machine Comprehension of Text. arXiv:1806.03822, 2018. Learning to ask good questions: Ranking clarification questions using neural expected value of perfect information. In Proceedings of the Association for Computational Linguistics. EMNLP 2016. paper (SQuAD 2.0) Know What You Don't Know: Unanswerable Questions for SQuAD. SQuAD: 100, 000+ Questions for Machine Comprehension of Text. Upload Slides slides or other attachment. Cited by. Know what you don’t know: Unanswerable Upload Slides Note: publisher must agree to add uploaded document . 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. Ground Truth Answer. 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. Models trained or fine-tuned on squad_v2. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 2016. Dekang Lin and Patrick Pantel. Verified email at cs.stanford.edu - Homepage. Learn more here; Loading the dataset using TensorFlow • Compared to under-incentivized humans. [2] Ashish Vaswani, et al. SQuAD: 100,000+ questions for machine comprehension of text. 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。 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. [63] Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, and Percy Liang. [3] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Predict & Visualize 0. It contains more than 100,000 question-answer pairs about passages from 536 … Discovery of inference rules for question-answering. SQuAD: 100, 000+ Questions for Machine Comprehension of Text. The current state of the art framework on the SQuAD dataset is SA-Net on Albert. Advances in Neural Information Processing Systems, 2017. team; license; privacy; imprint; manage site settings . 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