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Ranit Aharonov
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2020 – today
- 2022
- [c25]Liat Ein-Dor, Ilya Shnayderman, Artem Spector, Lena Dankin, Ranit Aharonov, Noam Slonim:
Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis. AAAI 2022: 10608-10617 - [c24]Elron Bandel, Ranit Aharonov, Michal Shmueli-Scheuer, Ilya Shnayderman, Noam Slonim, Liat Ein-Dor:
Quality Controlled Paraphrase Generation. ACL (1) 2022: 596-609 - [c23]Eyal Shnarch, Ariel Gera, Alon Halfon, Lena Dankin, Leshem Choshen, Ranit Aharonov, Noam Slonim:
Cluster & Tune: Boost Cold Start Performance in Text Classification. ACL (1) 2022: 7639-7653 - [i20]Liat Ein-Dor, Ilya Shnayderman, Artem Spector, Lena Dankin, Ranit Aharonov, Noam Slonim:
Fortunately, Discourse Markers Can Enhance Language Models for Sentiment Analysis. CoRR abs/2201.02026 (2022) - [i19]Eyal Shnarch, Ariel Gera, Alon Halfon, Lena Dankin, Leshem Choshen, Ranit Aharonov, Noam Slonim:
Cluster & Tune: Boost Cold Start Performance in Text Classification. CoRR abs/2203.10581 (2022) - [i18]Elron Bandel, Ranit Aharonov, Michal Shmueli-Scheuer, Ilya Shnayderman, Noam Slonim, Liat Ein-Dor:
Quality Controlled Paraphrase Generation. CoRR abs/2203.10940 (2022) - [i17]Eyal Shnarch, Alon Halfon, Ariel Gera, Marina Danilevsky, Yannis Katsis, Leshem Choshen, Martín Santillán Cooper, Dina Epelboim, Zheng Zhang, Dakuo Wang, Lucy Yip, Liat Ein-Dor, Lena Dankin, Ilya Shnayderman, Ranit Aharonov, Yunyao Li, Naftali Liberman, Philip Levin Slesarev, Gwilym Newton, Shila Ofek-Koifman, Noam Slonim, Yoav Katz:
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours. CoRR abs/2208.01483 (2022) - 2021
- [j2]Noam Slonim
, Yonatan Bilu, Carlos Alzate, Roy Bar-Haim, Ben Bogin, Francesca Bonin, Leshem Choshen, Edo Cohen-Karlik, Lena Dankin, Lilach Edelstein, Liat Ein-Dor, Roni Friedman-Melamed, Assaf Gavron, Ariel Gera, Martin Gleize, Shai Gretz, Dan Gutfreund, Alon Halfon, Daniel Hershcovich, Ron Hoory, Yufang Hou, Shay Hummel, Michal Jacovi, Charles Jochim, Yoav Kantor, Yoav Katz, David Konopnicki, Zvi Kons, Lili Kotlerman, Dalia Krieger, Dan Lahav, Tamar Lavee, Ran Levy
An autonomous debating system. Nat. 591(7850): 379-384 (2021) - [c22]Roni Friedman, Lena Dankin, Yufang Hou, Ranit Aharonov, Yoav Katz, Noam Slonim:
Overview of the 2021 Key Point Analysis Shared Task. ArgMining@EMNLP 2021: 154-164 - [c21]Guy Feigenblat, R. Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, Ranit Aharonov:
TWEETSUMM - A Dialog Summarization Dataset for Customer Service. EMNLP (Findings) 2021: 245-260 - [c20]Chulaka Gunasekara, Guy Feigenblat, Benjamin Sznajder, Ranit Aharonov, Sachindra Joshi:
Using Question Answering Rewards to Improve Abstractive Summarization. EMNLP (Findings) 2021: 518-526 - [c19]Matan Orbach, Orith Toledo-Ronen, Artem Spector, Ranit Aharonov, Yoav Katz, Noam Slonim:
YASO: A Targeted Sentiment Analysis Evaluation Dataset for Open-Domain Reviews. EMNLP (1) 2021: 9154-9173 - [i16]Roni Friedman, Lena Dankin, Yufang Hou, Ranit Aharonov, Yoav Katz, Noam Slonim:
Overview of the 2021 Key Point Analysis Shared Task. CoRR abs/2110.10577 (2021) - [i15]Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, Ranit Aharonov:
TWEETSUMM - A Dialog Summarization Dataset for Customer Service. CoRR abs/2111.11894 (2021) - 2020
- [c18]Liat Ein-Dor, Eyal Shnarch, Lena Dankin, Alon Halfon, Benjamin Sznajder, Ariel Gera, Carlos Alzate, Martin Gleize, Leshem Choshen, Yufang Hou, Yonatan Bilu, Ranit Aharonov, Noam Slonim:
Corpus Wide Argument Mining - A Working Solution. AAAI 2020: 7683-7691 - [c17]Shai Gretz, Roni Friedman, Edo Cohen-Karlik, Assaf Toledo, Dan Lahav, Ranit Aharonov, Noam Slonim:
A Large-Scale Dataset for Argument Quality Ranking: Construction and Analysis. AAAI 2020: 7805-7813 - [c16]Matan Orbach, Yonatan Bilu, Assaf Toledo, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim:
Out of the Echo Chamber: Detecting Countering Debate Speeches. ACL 2020: 7073-7086 - [c15]Eyal Shnarch, Leshem Choshen, Guy Moshkowich, Ranit Aharonov, Noam Slonim:
Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains. EMNLP (Findings) 2020: 2678-2697 - [c14]Liat Ein-Dor, Alon Halfon, Ariel Gera, Eyal Shnarch, Lena Dankin, Leshem Choshen, Marina Danilevsky, Ranit Aharonov, Yoav Katz, Noam Slonim:
Active Learning for BERT: An Empirical Study. EMNLP (1) 2020: 7949-7962 - [c13]Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, Prithviraj Sen:
A Survey of the State of Explainable AI for Natural Language Processing. AACL/IJCNLP 2020: 447-459 - [i14]Matan Orbach, Yonatan Bilu, Assaf Toledo, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim:
Out of the Echo Chamber: Detecting Countering Debate Speeches. CoRR abs/2005.01157 (2020) - [i13]Marina Danilevsky, Kun Qian, Ranit Aharonov, Yannis Katsis, Ban Kawas, Prithviraj Sen:
A Survey of the State of Explainable AI for Natural Language Processing. CoRR abs/2010.00711 (2020) - [i12]Eyal Shnarch, Leshem Choshen, Guy Moshkowich, Noam Slonim, Ranit Aharonov:
Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains. CoRR abs/2010.09459 (2020) - [i11]Ishan Jindal, Ranit Aharonov, Siddhartha Brahma, Huaiyu Zhu, Yunyao Li:
Improved Semantic Role Labeling using Parameterized Neighborhood Memory Adaptation. CoRR abs/2011.14459 (2020) - [i10]Matan Orbach, Orith Toledo-Ronen, Artem Spector, Ranit Aharonov, Yoav Katz, Noam Slonim:
YASO: A New Benchmark for Targeted Sentiment Analysis. CoRR abs/2012.14541 (2020)
2010 – 2019
- 2019
- [c12]Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, Noam Slonim:
Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network. ACL (1) 2019: 967-976 - [c11]Roy Bar-Haim, Dalia Krieger, Orith Toledo-Ronen, Lilach Edelstein, Yonatan Bilu, Alon Halfon, Yoav Katz, Amir Menczel, Ranit Aharonov, Noam Slonim:
From Surrogacy to Adoption; From Bitcoin to Cryptocurrency: Debate Topic Expansion. ACL (1) 2019: 977-990 - [c10]Tamar Lavee, Matan Orbach, Lili Kotlerman, Yoav Kantor, Shai Gretz, Lena Dankin, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim:
Towards Effective Rebuttal: Listening Comprehension Using Corpus-Wide Claim Mining. ArgMining@ACL 2019: 58-66 - [c9]Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim:
A Dataset of General-Purpose Rebuttal. EMNLP/IJCNLP (1) 2019: 5590-5600 - [c8]Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim:
Automatic Argument Quality Assessment - New Datasets and Methods. EMNLP/IJCNLP (1) 2019: 5624-5634 - [i9]Martin Gleize, Eyal Shnarch, Leshem Choshen, Lena Dankin, Guy Moshkowich, Ranit Aharonov, Noam Slonim:
Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network. CoRR abs/1907.08971 (2019) - [i8]Tamar Lavee, Matan Orbach, Lili Kotlerman, Yoav Kantor, Shai Gretz, Lena Dankin, Shachar Mirkin, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim:
Towards Effective Rebuttal: Listening Comprehension using Corpus-Wide Claim Mining. CoRR abs/1907.11889 (2019) - [i7]Ilya Shnayderman, Liat Ein-Dor, Yosi Mass, Alon Halfon, Benjamin Sznajder, Artem Spector, Yoav Katz, Dafna Sheinwald, Ranit Aharonov, Noam Slonim:
Fast End-to-End Wikification. CoRR abs/1908.06785 (2019) - [i6]Benjamin Sznajder, Ariel Gera, Yonatan Bilu, Dafna Sheinwald, Ella Rabinovich, Ranit Aharonov, David Konopnicki, Noam Slonim:
Controversy in Context. CoRR abs/1908.07491 (2019) - [i5]Matan Orbach, Yonatan Bilu, Ariel Gera, Yoav Kantor, Lena Dankin, Tamar Lavee, Lili Kotlerman, Shachar Mirkin, Michal Jacovi, Ranit Aharonov, Noam Slonim:
A Dataset of General-Purpose Rebuttal. CoRR abs/1909.00393 (2019) - [i4]Assaf Toledo, Shai Gretz, Edo Cohen-Karlik, Roni Friedman, Elad Venezian, Dan Lahav, Michal Jacovi, Ranit Aharonov, Noam Slonim:
Automatic Argument Quality Assessment - New Datasets and Methods. CoRR abs/1909.01007 (2019) - [i3]Liat Ein-Dor, Eyal Shnarch, Lena Dankin, Alon Halfon, Benjamin Sznajder, Ariel Gera, Carlos Alzate, Martin Gleize, Leshem Choshen, Yufang Hou, Yonatan Bilu, Ranit Aharonov, Noam Slonim:
Corpus Wide Argument Mining - a Working Solution. CoRR abs/1911.10763 (2019) - [i2]Shai Gretz, Roni Friedman, Edo Cohen-Karlik, Assaf Toledo, Dan Lahav, Ranit Aharonov, Noam Slonim:
A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis. CoRR abs/1911.11408 (2019) - 2018
- [c7]Liat Ein-Dor, Yosi Mass, Alon Halfon, Elad Venezian, Ilya Shnayderman, Ranit Aharonov, Noam Slonim:
Learning Thematic Similarity Metric from Article Sections Using Triplet Networks. ACL (2) 2018: 49-54 - [c6]Eyal Shnarch, Carlos Alzate, Lena Dankin, Martin Gleize, Yufang Hou, Leshem Choshen, Ranit Aharonov, Noam Slonim:
Will it Blend? Blending Weak and Strong Labeled Data in a Neural Network for Argumentation Mining. ACL (2) 2018: 599-605 - [c5]Ran Levy, Ben Bogin, Shai Gretz, Ranit Aharonov, Noam Slonim:
Towards an argumentative content search engine using weak supervision. COLING 2018: 2066-2081 - [c4]Orith Toledo-Ronen, Roy Bar-Haim, Alon Halfon, Charles Jochim, Amir Menczel, Ranit Aharonov, Noam Slonim:
Learning Sentiment Composition from Sentiment Lexicons. COLING 2018: 2230-2241 - [c3]Shachar Mirkin, Guy Moshkowich, Matan Orbach, Lili Kotlerman, Yoav Kantor, Tamar Lavee, Michal Jacovi, Yonatan Bilu, Ranit Aharonov, Noam Slonim:
Listening Comprehension over Argumentative Content. EMNLP 2018: 719-724 - [c2]Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim:
Learning Concept Abstractness Using Weak Supervision. EMNLP 2018: 4854-4859 - [e1]Noam Slonim, Ranit Aharonov:
Proceedings of the 5th Workshop on Argument Mining, ArgMining@EMNLP 2018, Brussels, Belgium, November 1, 2018. Association for Computational Linguistics 2018, ISBN 978-1-948087-69-8 [contents] - [i1]Ella Rabinovich, Benjamin Sznajder, Artem Spector, Ilya Shnayderman, Ranit Aharonov, David Konopnicki, Noam Slonim:
Learning Concept Abstractness Using Weak Supervision. CoRR abs/1809.01285 (2018) - 2017
- [c1]Ran Levy
Unsupervised corpus-wide claim detection. ArgMining@EMNLP 2017: 79-84 - 2016
- [j1]Michal Ozery-Flato
Identifying and Investigating Unexpected Response to Treatment: A Diabetes Case Study. Big Data 4(3): 148-159 (2016)
Coauthor Index
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