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Gabriel Stanovsky
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- affiliation: The Hebrew University of Jerusalem, Israel
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2020 – today
- 2024
- [j2]Moran Mizrahi, Guy Kaplan, Dan Malkin, Rotem Dror, Dafna Shahaf, Gabriel Stanovsky:
State of What Art? A Call for Multi-Prompt LLM Evaluation. Trans. Assoc. Comput. Linguistics 12: 933-949 (2024) - [c53]Ariel Goldstein, Gabriel Stanovsky:
Do Zombies Understand? A Choose-Your-Own-Adventure Exploration of Machine Cognition. ACL (Findings) 2024: 7137-7143 - [c52]Gili Lior, Yoav Goldberg, Gabriel Stanovsky:
Leveraging Collection-Wide Similarities for Unsupervised Document Structure Extraction. ACL (Findings) 2024: 9538-9550 - [c51]Itay Manes, Naama Ronn, David Cohen, Ran Ilan Ber, Zehavi Horowitz-Kugler, Gabriel Stanovsky:
K-QA: A Real-World Medical Q&A Benchmark. BioNLP@ACL 2024: 277-294 - [c50]Fan Bai, Junmo Kang, Gabriel Stanovsky, Dayne Freitag, Mark Dredze, Alan Ritter:
Schema-Driven Information Extraction from Heterogeneous Tables. EMNLP (Findings) 2024: 10252-10273 - [c49]Bar Iluz, Yanai Elazar, Asaf Yehudai, Gabriel Stanovsky:
Applying Intrinsic Debiasing on Downstream Tasks: Challenges and Considerations for Machine Translation. EMNLP 2024: 14914-14921 - [i49]Moran Mizrahi, Guy Kaplan, Dan Malkin, Rotem Dror, Dafna Shahaf, Gabriel Stanovsky:
State of What Art? A Call for Multi-Prompt LLM Evaluation. CoRR abs/2401.00595 (2024) - [i48]Itay Manes, Naama Ronn, David Cohen, Ran Ilan Ber, Zehavi Horowitz-Kugler, Gabriel Stanovsky:
K-QA: A Real-World Medical Q&A Benchmark. CoRR abs/2401.14493 (2024) - [i47]Gili Lior, Yoav Goldberg, Gabriel Stanovsky:
Leveraging Collection-Wide Similarities for Unsupervised Document Structure Extraction. CoRR abs/2402.13906 (2024) - [i46]Ariel Goldstein, Gabriel Stanovsky:
Do Zombies Understand? A Choose-Your-Own-Adventure Exploration of Machine Cognition. CoRR abs/2403.00499 (2024) - [i45]Asaf Yehudai, Taelin Karidi, Gabriel Stanovsky, Ariel Goldstein, Omri Abend:
A Nurse is Blue and Elephant is Rugby: Cross Domain Alignment in Large Language Models Reveal Human-like Patterns. CoRR abs/2405.14863 (2024) - [i44]Bar Iluz, Yanai Elazar, Asaf Yehudai, Gabriel Stanovsky:
Applying Intrinsic Debiasing on Downstream Tasks: Challenges and Considerations for Machine Translation. CoRR abs/2406.00787 (2024) - [i43]Uri Berger, Tal Baumel, Gabriel Stanovsky:
In-Context Learning on a Budget: A Case Study in Named Entity Recognition. CoRR abs/2406.13274 (2024) - [i42]Gili Lior, Avi Caciularu, Arie Cattan, Shahar Levy, Ori Shapira, Gabriel Stanovsky:
SEAM: A Stochastic Benchmark for Multi-Document Tasks. CoRR abs/2406.16086 (2024) - [i41]Uri Berger, Gabriel Stanovsky, Omri Abend, Lea Frermann:
Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy and Novel Ensemble Method. CoRR abs/2408.04909 (2024) - [i40]Daria Lioubashevski, Tomer Schlank, Gabriel Stanovsky, Ariel Goldstein:
Looking Beyond The Top-1: Transformers Determine Top Tokens In Order. CoRR abs/2410.20210 (2024) - 2023
- [c48]Yonatan Bitton, Ron Yosef, Eliyahu Strugo, Dafna Shahaf, Roy Schwartz, Gabriel Stanovsky:
VASR: Visual Analogies of Situation Recognition. AAAI 2023: 241-249 - [c47]Catherine Chen, Zejiang Shen, Dan Klein, Gabriel Stanovsky, Doug Downey, Kyle Lo:
Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents. ACL (Findings) 2023: 13345-13360 - [c46]Gili Lior, Gabriel Stanovsky:
Comparing Humans and Models on a Similar Scale: Towards Cognitive Gender Bias Evaluation in Coreference Resolution. CogSci 2023 - [c45]Asaf Yehudai, Arie Cattan, Omri Abend, Gabriel Stanovsky:
Evaluating and Improving the Coreference Capabilities of Machine Translation Models. EACL 2023: 980-992 - [c44]Uri Berger, Lea Frermann, Gabriel Stanovsky, Omri Abend:
A Large-Scale Multilingual Study of Visual Constraints on Linguistic Selection of Descriptions. EACL (Findings) 2023: 2240-2254 - [c43]Eliya Habba, Renana Keydar, Dan Bareket, Gabriel Stanovsky:
The Perfect Victim: Computational Analysis of Judicial Attitudes towards Victims of Sexual Violence. ICAIL 2023: 111-120 - [c42]Nitzan Bitton Guetta, Yonatan Bitton, Jack Hessel, Ludwig Schmidt, Yuval Elovici, Gabriel Stanovsky, Roy Schwartz:
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images. ICCV 2023: 2616-2627 - [c41]Bar Iluz, Tomasz Limisiewicz, Gabriel Stanovsky, David Marecek:
Exploring the Impact of Training Data Distribution and Subword Tokenization on Gender Bias in Machine Translation. IJCNLP (1) 2023: 885-896 - [i39]Uri Berger, Lea Frermann, Gabriel Stanovsky, Omri Abend:
A Large-Scale Multilingual Study of Visual Constraints on Linguistic Selection of Descriptions. CoRR abs/2302.04811 (2023) - [i38]Asaf Yehudai, Arie Cattan, Omri Abend, Gabriel Stanovsky:
Evaluating and Improving the Coreference Capabilities of Machine Translation Models. CoRR abs/2302.08464 (2023) - [i37]Nitzan Bitton Guetta, Yonatan Bitton, Jack Hessel, Ludwig Schmidt, Yuval Elovici, Gabriel Stanovsky, Roy Schwartz:
Breaking Common Sense: WHOOPS! A Vision-and-Language Benchmark of Synthetic and Compositional Images. CoRR abs/2303.07274 (2023) - [i36]Eliya Habba, Renana Keydar, Dan Bareket, Gabriel Stanovsky:
The Perfect Victim: Computational Analysis of Judicial Attitudes towards Victims of Sexual Violence. CoRR abs/2305.05302 (2023) - [i35]Fan Bai, Junmo Kang, Gabriel Stanovsky, Dayne Freitag, Alan Ritter:
Schema-Driven Information Extraction from Heterogeneous Tables. CoRR abs/2305.14336 (2023) - [i34]Gili Lior, Gabriel Stanovsky:
Comparing Humans and Models on a Similar Scale: Towards Cognitive Gender Bias Evaluation in Coreference Resolution. CoRR abs/2305.15389 (2023) - [i33]Catherine Chen, Zejiang Shen, Dan Klein, Gabriel Stanovsky, Doug Downey, Kyle Lo:
Are Layout-Infused Language Models Robust to Layout Distribution Shifts? A Case Study with Scientific Documents. CoRR abs/2306.01058 (2023) - [i32]Itay Itzhak, Gabriel Stanovsky, Nir Rosenfeld, Yonatan Belinkov:
Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias. CoRR abs/2308.00225 (2023) - [i31]Bar Iluz, Tomasz Limisiewicz, Gabriel Stanovsky, David Marecek:
Exploring the Impact of Training Data Distribution and Subword Tokenization on Gender Bias in Machine Translation. CoRR abs/2309.12491 (2023) - 2022
- [c40]Keshav Kolluru, Gabriel Stanovsky, Mausam:
"Covid vaccine is against Covid but Oxford vaccine is made at Oxford!" Semantic Interpretation of Proper Noun Compounds. EMNLP 2022: 10407-10420 - [c39]Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, Daniel S. Weld:
GENIE: Toward Reproducible and Standardized Human Evaluation for Text Generation. EMNLP 2022: 11444-11458 - [c38]Roy Schwartz, Gabriel Stanovsky:
On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations. NAACL-HLT (Findings) 2022: 2182-2194 - [c37]Uri Berger, Gabriel Stanovsky, Omri Abend, Lea Frermann:
A Computational Acquisition Model for Multimodal Word Categorization. NAACL-HLT 2022: 3819-3835 - [c36]Dan Malkin, Tomasz Limisiewicz, Gabriel Stanovsky:
A Balanced Data Approach for Evaluating Cross-Lingual Transfer: Mapping the Linguistic Blood Bank. NAACL-HLT 2022: 4903-4915 - [c35]Yonatan Bitton, Nitzan Bitton Guetta, Ron Yosef, Yuval Elovici, Mohit Bansal, Gabriel Stanovsky, Roy Schwartz:
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models. NeurIPS 2022 - [c34]Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan:
Proceedings of the 16th International Workshop on Semantic Evaluation (SemEval-2022). SemEval@NAACL 2022 - [e1]Guy Emerson, Natalie Schluter, Gabriel Stanovsky, Ritesh Kumar, Alexis Palmer, Nathan Schneider, Siddharth Singh, Shyam Ratan:
Proceedings of the 16th International Workshop on Semantic Evaluation, SemEval@NAACL 2022, Seattle, Washington, United States, July 14-15, 2022. Association for Computational Linguistics 2022, ISBN 978-1-955917-80-3 [contents] - [i30]Roy Schwartz, Gabriel Stanovsky:
On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations. CoRR abs/2204.12708 (2022) - [i29]Dan Malkin, Tomasz Limisiewicz, Gabriel Stanovsky:
A Balanced Data Approach for Evaluating Cross-Lingual Transfer: Mapping the Linguistic Blood Bank. CoRR abs/2205.04086 (2022) - [i28]Uri Berger, Gabriel Stanovsky, Omri Abend, Lea Frermann:
A Computational Acquisition Model for Multimodal Word Categorization. CoRR abs/2205.05974 (2022) - [i27]Yonatan Bitton, Nitzan Bitton Guetta, Ron Yosef, Yuval Elovici, Mohit Bansal, Gabriel Stanovsky, Roy Schwartz:
WinoGAViL: Gamified Association Benchmark to Challenge Vision-and-Language Models. CoRR abs/2207.12576 (2022) - [i26]Tomasz Limisiewicz, Dan Malkin, Gabriel Stanovsky:
You Can Have Your Data and Balance It Too: Towards Balanced and Efficient Multilingual Models. CoRR abs/2210.07135 (2022) - [i25]Keshav Kolluru, Gabriel Stanovsky, Mausam:
"Covid vaccine is against Covid but Oxford vaccine is made at Oxford!" Semantic Interpretation of Proper Noun Compounds. CoRR abs/2210.13039 (2022) - [i24]Yonatan Bitton, Ron Yosef, Eli Strugo, Dafna Shahaf, Roy Schwartz, Gabriel Stanovsky:
VASR: Visual Analogies of Situation Recognition. CoRR abs/2212.04542 (2022) - 2021
- [j1]Lucy Lu Wang, Gabriel Stanovsky, Luca Weihs, Oren Etzioni:
Gender trends in computer science authorship. Commun. ACM 64(3): 78-84 (2021) - [c33]Arie Cattan, Alon Eirew, Gabriel Stanovsky, Mandar Joshi, Ido Dagan:
Cross-document Coreference Resolution over Predicted Mentions. ACL/IJCNLP (Findings) 2021: 5100-5107 - [c32]Mohr Wenger, Tom Kalir, Noga Berger, Carmit Klar Chalamish, Renana Keydar, Gabriel Stanovsky:
Automated Extraction of Sentencing Decisions from Court Cases in the Hebrew Language. NLLP@EMNLP 2021: 36-45 - [c31]Ronen Tamari, Fan Bai, Alan Ritter, Gabriel Stanovsky:
Process-Level Representation of Scientific Protocols with Interactive Annotation. EACL 2021: 2190-2202 - [c30]Shahar Levy, Koren Lazar, Gabriel Stanovsky:
Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation. EMNLP (Findings) 2021: 2470-2480 - [c29]Yonatan Bitton, Michael Elhadad, Gabriel Stanovsky, Roy Schwartz:
Data Efficient Masked Language Modeling for Vision and Language. EMNLP (Findings) 2021: 3013-3028 - [c28]Koren Lazar, Benny Saret, Asaf Yehudai, Wayne Horowitz, Nathan Wasserman, Gabriel Stanovsky:
Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approach. EMNLP (1) 2021: 4682-4691 - [c27]Yonatan Bitton, Gabriel Stanovsky, Roy Schwartz, Michael Elhadad:
Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQA. NAACL-HLT 2021: 94-105 - [c26]Arie Cattan, Alon Eirew, Gabriel Stanovsky, Mandar Joshi, Ido Dagan:
Realistic Evaluation Principles for Cross-document Coreference Resolution. *SEM 2021: 143-151 - [i23]Daniel Khashabi, Gabriel Stanovsky, Jonathan Bragg, Nicholas Lourie, Jungo Kasai, Yejin Choi, Noah A. Smith, Daniel S. Weld:
GENIE: A Leaderboard for Human-in-the-Loop Evaluation of Text Generation. CoRR abs/2101.06561 (2021) - [i22]Ronen Tamari, Fan Bai, Alan Ritter, Gabriel Stanovsky:
Process-Level Representation of Scientific Protocols with Interactive Annotation. CoRR abs/2101.10244 (2021) - [i21]Yonatan Bitton, Gabriel Stanovsky, Roy Schwartz, Michael Elhadad:
Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQA. CoRR abs/2103.09591 (2021) - [i20]Arie Cattan, Alon Eirew, Gabriel Stanovsky, Mandar Joshi, Ido Dagan:
Cross-document Coreference Resolution over Predicted Mentions. CoRR abs/2106.01210 (2021) - [i19]Arie Cattan, Alon Eirew, Gabriel Stanovsky, Mandar Joshi, Ido Dagan:
Realistic Evaluation Principles for Cross-document Coreference Resolution. CoRR abs/2106.04192 (2021) - [i18]Yonatan Bitton, Gabriel Stanovsky, Michael Elhadad, Roy Schwartz:
Data Efficient Masked Language Modeling for Vision and Language. CoRR abs/2109.02040 (2021) - [i17]Shahar Levy, Koren Lazar, Gabriel Stanovsky:
Collecting a Large-Scale Gender Bias Dataset for Coreference Resolution and Machine Translation. CoRR abs/2109.03858 (2021) - [i16]Koren Lazar, Benny Saret, Asaf Yehudai, Wayne Horowitz, Nathan Wasserman, Gabriel Stanovsky:
Filling the Gaps in Ancient Akkadian Texts: A Masked Language Modelling Approach. CoRR abs/2109.04513 (2021) - [i15]Mohr Wenger, Tom Kalir, Noga Berger, Carmit Klar Chalamish, Renana Keydar, Gabriel Stanovsky:
Automated Extraction of Sentencing Decisions from Court Cases in the Hebrew Language. CoRR abs/2110.12383 (2021) - 2020
- [c25]Roy Schwartz, Gabriel Stanovsky, Swabha Swayamdipta, Jesse Dodge, Noah A. Smith:
The Right Tool for the Job: Matching Model and Instance Complexities. ACL 2020: 6640-6651 - [c24]Paul Roit, Ayal Klein, Daniela Stepanov, Jonathan Mamou, Julian Michael, Gabriel Stanovsky, Luke Zettlemoyer, Ido Dagan:
Controlled Crowdsourcing for High-Quality QA-SRL Annotation. ACL 2020: 7008-7013 - [c23]Belinda Z. Li, Gabriel Stanovsky, Luke Zettlemoyer:
Active Learning for Coreference Resolution using Discrete Annotation. ACL 2020: 8320-8331 - [c22]Anthony Chen, Gabriel Stanovsky, Sameer Singh, Matt Gardner:
MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics. EMNLP (1) 2020: 6521-6532 - [c21]Tom Kocmi, Tomasz Limisiewicz, Gabriel Stanovsky:
Gender Coreference and Bias Evaluation at WMT 2020. WMT@EMNLP 2020: 357-364 - [i14]Ronen Tamari, Gabriel Stanovsky, Dafna Shahaf, Reut Tsarfaty:
Ecological Semantics: Programming Environments for Situated Language Understanding. CoRR abs/2003.04567 (2020) - [i13]Roy Schwartz, Gabi Stanovsky, Swabha Swayamdipta, Jesse Dodge, Noah A. Smith:
The Right Tool for the Job: Matching Model and Instance Complexities. CoRR abs/2004.07453 (2020) - [i12]Belinda Z. Li, Gabriel Stanovsky, Luke Zettlemoyer:
Active Learning for Coreference Resolution using Discrete Annotation. CoRR abs/2004.13671 (2020) - [i11]Arie Cattan, Alon Eirew, Gabriel Stanovsky, Mandar Joshi, Ido Dagan:
Streamlining Cross-Document Coreference Resolution: Evaluation and Modeling. CoRR abs/2009.11032 (2020) - [i10]Anthony Chen, Gabriel Stanovsky, Sameer Singh, Matt Gardner:
MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics. CoRR abs/2010.03636 (2020) - [i9]Tom Kocmi, Tomasz Limisiewicz, Gabriel Stanovsky:
Gender Coreference and Bias Evaluation at WMT 2020. CoRR abs/2010.06018 (2020)
2010 – 2019
- 2019
- [c20]Gabriel Stanovsky, Noah A. Smith, Luke Zettlemoyer:
Evaluating Gender Bias in Machine Translation. ACL (1) 2019: 1679-1684 - [c19]Anthony Chen, Gabriel Stanovsky, Sameer Singh, Matt Gardner:
Evaluating Question Answering Evaluation. MRQA@EMNLP 2019: 119-124 - [c18]Gabriel Stanovsky, Ronen Tamari:
Y'all should read this! Identifying Plurality in Second-Person Personal Pronouns in English Texts. W-NUT@EMNLP 2019: 375-380 - [c17]Omri Koshorek, Gabriel Stanovsky, Yichu Zhou, Vivek Srikumar, Jonathan Berant:
On the Limits of Learning to Actively Learn Semantic Representations. CoNLL 2019: 452-462 - [c16]Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner:
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs. NAACL-HLT (1) 2019: 2368-2378 - [c15]Mark Hopkins, Ronan Le Bras, Cristian Petrescu-Prahova, Gabriel Stanovsky, Hannaneh Hajishirzi, Rik Koncel-Kedziorski:
SemEval-2019 Task 10: Math Question Answering. SemEval@NAACL-HLT 2019: 893-899 - [i8]Dheeru Dua, Yizhong Wang, Pradeep Dasigi, Gabriel Stanovsky, Sameer Singh, Matt Gardner:
DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs. CoRR abs/1903.00161 (2019) - [i7]Gabriel Stanovsky, Noah A. Smith, Luke Zettlemoyer:
Evaluating Gender Bias in Machine Translation. CoRR abs/1906.00591 (2019) - [i6]Lucy Lu Wang, Gabriel Stanovsky, Luca Weihs, Oren Etzioni:
Gender trends in computer science authorship. CoRR abs/1906.07883 (2019) - [i5]Omri Koshorek, Gabriel Stanovsky, Yichu Zhou, Vivek Srikumar, Jonathan Berant:
On the Limits of Learning to Actively Learn Semantic Representations. CoRR abs/1910.02228 (2019) - [i4]Gabriel Stanovsky, Ronen Tamari:
Yall should read this! Identifying Plurality in Second-Person Personal Pronouns in English Texts. CoRR abs/1910.11966 (2019) - [i3]Paul Roit, Ayal Klein, Daniela Stepanov, Jonathan Mamou, Julian Michael, Gabriel Stanovsky, Luke Zettlemoyer, Ido Dagan:
Crowdsourcing a High-Quality Gold Standard for QA-SRL. CoRR abs/1911.03243 (2019) - 2018
- [c14]Gabriel Stanovsky, Mark Hopkins:
Spot the Odd Man Out: Exploring the Associative Power of Lexical Resources. EMNLP 2018: 1533-1542 - [c13]Gabriel Stanovsky, Ido Dagan:
Semantics as a Foreign Language. EMNLP 2018: 2412-2421 - [c12]Julian Michael, Gabriel Stanovsky, Luheng He, Ido Dagan, Luke Zettlemoyer:
Crowdsourcing Question-Answer Meaning Representations. NAACL-HLT (2) 2018: 560-568 - [c11]Gabriel Stanovsky, Julian Michael, Luke Zettlemoyer, Ido Dagan:
Supervised Open Information Extraction. NAACL-HLT 2018: 885-895 - 2017
- [c10]Gabriel Stanovsky, Judith Eckle-Kohler, Yevgeniy Puzikov, Ido Dagan, Iryna Gurevych:
Integrating Deep Linguistic Features in Factuality Prediction over Unified Datasets. ACL (2) 2017: 352-357 - [c9]Rachel Wities, Vered Shwartz, Gabriel Stanovsky, Meni Adler, Ori Shapira, Shyam Upadhyay, Dan Roth, Eugenio Martínez-Cámara, Iryna Gurevych, Ido Dagan:
A Consolidated Open Knowledge Representation for Multiple Texts. LSDSem@EACL 2017: 12-24 - [c8]Gabriel Stanovsky, Daniel Gruhl, Pablo N. Mendes:
Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models. EACL (1) 2017: 142-151 - [c7]Vered Shwartz, Gabriel Stanovsky, Ido Dagan:
Acquiring Predicate Paraphrases from News Tweets. *SEM 2017: 155-160 - [i2]Julian Michael, Gabriel Stanovsky, Luheng He, Ido Dagan, Luke Zettlemoyer:
Crowdsourcing Question-Answer Meaning Representations. CoRR abs/1711.05885 (2017) - 2016
- [c6]Gabriel Stanovsky, Ido Dagan:
Annotating and Predicting Non-Restrictive Noun Phrase Modifications. ACL (1) 2016 - [c5]Gabriel Stanovsky, Ido Dagan, Meni Adler:
Specifying and Annotating Reduced Argument Span Via QA-SRL. ACL (2) 2016 - [c4]Omer Levy, Ido Dagan, Gabriel Stanovsky, Judith Eckle-Kohler, Iryna Gurevych:
Modeling Extractive Sentence Intersection via Subtree Entailment. COLING 2016: 2891-2901 - [c3]Tobias Falke, Gabriel Stanovsky, Iryna Gurevych, Ido Dagan:
Porting an Open Information Extraction System from English to German. EMNLP 2016: 892-898 - [c2]Gabriel Stanovsky, Ido Dagan:
Creating a Large Benchmark for Open Information Extraction. EMNLP 2016: 2300-2305 - [i1]Gabriel Stanovsky, Jessica Ficler, Ido Dagan, Yoav Goldberg:
Getting More Out Of Syntax with PropS. CoRR abs/1603.01648 (2016) - 2015
- [c1]Gabriel Stanovsky, Ido Dagan, Mausam:
Open IE as an Intermediate Structure for Semantic Tasks. ACL (2) 2015: 303-308
Coauthor Index
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