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pro vyhledávání: '"Kantor, Amir"'
We present MeeQA, a dataset for natural-language question answering over meeting transcripts. It includes real questions asked during meetings by its participants. The dataset contains 48K question-answer pairs, extracted from 422 meeting transcripts
Externí odkaz:
http://arxiv.org/abs/2305.08502
Modern Natural Language Generation (NLG) models come with massive computational and storage requirements. In this work, we study the potential of compressing them, which is crucial for real-world applications serving millions of users. We focus on Kn
Externí odkaz:
http://arxiv.org/abs/2305.02031
Autor:
Anaby-Tavor, Ateret, Carmeli, Boaz, Goldbraich, Esther, Kantor, Amir, Kour, George, Shlomov, Segev, Tepper, Naama, Zwerdling, Naama
Based on recent advances in natural language modeling and those in text generation capabilities, we propose a novel data augmentation method for text classification tasks. We use a powerful pre-trained neural network model to artificially synthesize
Externí odkaz:
http://arxiv.org/abs/1911.03118
Autor:
Harel, David, Kantor, Amir
Publikováno v:
In Theoretical Computer Science 20 April 2012 429:118-127
Akademický článek
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Autor:
Harel, David, Kantor, Amir
Publikováno v:
Language, Culture, Computation. Computing - Theory & Technology; 2014, p156-167, 12p
Publikováno v:
14-th IEEE International Conference on Peer-to-Peer Computing; 2014, p1-10, 10p
Publikováno v:
2013 Proceedings of the International Conference on Embedded Software (EMSOFT); 2013, p1-10, 10p
Publikováno v:
Logic for Programming, Artificial Intelligence & Reasoning: 19th International Conference, LPAR-19, Stellenbosch, South Africa, December 14-19, 2013, Proceedings; 2013, p355-372, 18p
Publikováno v:
Concurrency, Compositionality & Correctness; 2010, p207-220, 14p