squad percy liang

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