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