Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Leshem Choshen"'
Autor:
Tamar Lavee, Daniel Hershcovich, Slava Shechtman, Yoav Katz, Guy Moshkowich, Shai Gretz, Yonatan Bilu, Ran Levy, Charles Jochim, Michal Jacovi, Lilach Edelstein, Assaf Toledo, Ariel Gera, Artem Spector, Eyal Shnarch, Aya Soffer, Benjamin Sznajder, Carlos Alzate, Ruty Rinott, Orith Toledo-Ronen, Noam Slonim, Dalia Krieger, Dan Gutfreund, Yosi Mass, Lili Kotlerman, Ranit Aharonov, Liat Ein-Dor, Dafna Sheinwald, Leshem Choshen, Dan Lahav, Amir Menczel, Ben Bogin, David Konopnicki, Yoav Kantor, Shay Hummel, Elad Venezian, Shachar Mirkin, Ilya Shnayderman, Ron Hoory, Roni Friedman-Melamed, Lena Dankin, Martin Gleize, Roy Bar-Haim, Francesca Bonin, Ella Rabinovich, Matan Orbach, Zvi Kons, Alon Halfon, Yufang Hou, Shila Ofek-Koifman, Naftali Liberman, Edo Cohen-Karlik, Assaf Gavron
Publikováno v:
Nature. 591:379-384
Artificial intelligence (AI) is defined as the ability of machines to perform tasks that are usually associated with intelligent beings. Argument and debate are fundamental capabilities of human intelligence, essential for a wide range of human activ
Autor:
Yufang Hou, Benjamin Sznajder, Eyal Shnarch, Liat Ein-Dor, Noam Slonim, Lena Dankin, Carlos Alzate, Martin Gleize, Ariel Gera, Ranit Aharonov, Yonatan Bilu, Alon Halfon, Leshem Choshen
Publikováno v:
AAAI
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively small number of relevant documents as the initial source for such conten
Neural knowledge-grounded generative models for dialogue often produce content that is factually inconsistent with the knowledge they rely on, making them unreliable and limiting their applicability. Inspired by recent work on evaluating factual cons
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ac3b4e9c7c19bc32c02c0f57d4ec93cd
http://arxiv.org/abs/2104.08202
http://arxiv.org/abs/2104.08202
Publikováno v:
NAACL-HLT
Probing neural models for the ability to perform downstream tasks using their activation patterns is often used to localize what parts of the network specialize in performing what tasks. However, little work addressed potential mediating factors in s
The learning trajectories of linguistic phenomena in humans provide insight into linguistic representation, beyond what can be gleaned from inspecting the behavior of an adult speaker. To apply a similar approach to analyze neural language models (NL
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8b040b516ae6d809c50dfc96c32a373e
Publikováno v:
EMNLP (Findings)
Approaching new data can be quite deterrent; you do not know how your categories of interest are realized in it, commonly, there is no labeled data at hand, and the performance of domain adaptation methods is unsatisfactory. Aiming to assist domain e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6730d762743c4e06942326c979760832
http://arxiv.org/abs/2010.09459
http://arxiv.org/abs/2010.09459
Autor:
Leshem Choshen, Liat Ein-Dor, Lena Dankin, Eyal Shnarch, Alon Halfon, Ranit Aharonov, Yoav Katz, Marina Danilevsky, Ariel Gera, Noam Slonim
Publikováno v:
EMNLP (1)
Real world scenarios present a challenge for text classification, since labels are usually expensive and the data is often characterized by class imbalance. Active Learning (AL) is a ubiquitous paradigm to cope with data scarcity. Recently, pre-train
Publikováno v:
CoNLL
We present a method for classifying syntactic errors in learner language, namely errors whose correction alters the morphosyntactic structure of a sentence. The methodology builds on the established Universal Dependencies syntactic representation sch
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::05ee9d00b39b7a2e5958deeda1b43aff
Autor:
Amir Menczel, Noam Slonim, Yoav Kantor, Leshem Choshen, Assaf Toledo, Naftali Liberman, Yoav Katz, Edo Cohen-Karlik
Publikováno v:
BEA@ACL
The field of Grammatical Error Correction (GEC) has produced various systems to deal with focused phenomena or general text editing. We propose an automatic way to combine black-box systems. Our method automatically detects the strength of a system o
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::04e961ba4a21de67477d965dfb53e75a
http://arxiv.org/abs/1906.03897
http://arxiv.org/abs/1906.03897
Publikováno v:
SemEval@NAACL-HLT
We present the SemEval 2019 shared task on UCCA parsing in English, German and French, and discuss the participating systems and results. UCCA is a cross-linguistically applicable framework for semantic representation, which builds on extensive typol
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::bf2a0e8a3d90971e422387bf51245172