Zobrazeno 1 - 10
of 10
pro vyhledávání: '"Kalpesh Krishna"'
Autor:
Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahadiran, Simon Mille, Ashish Shrivastava, Samson Tan, null Tongshang Wu, Jascha Sohl-Dickstein, Jinho Choi, Eduard Hovy, Ondřej Dušek, Sebastian Ruder, Sajant Anand, Nagender Aneja, Rabin Banjade, Lisa Barthe, Hanna Behnke, Ian Berlot-Attwell, Connor Boyle, Caroline Brun, Marco Antonio Sobrevilla Cabezudo, Samuel Cahyawijaya, Emile Chapuis, Wanxiang Che, Mukund Choudhary, Christian Clauss, Pierre Colombo, Filip Cornell, Gautier Dagan, Mayukh Das, Tanay Dixit, Thomas Dopierre, Paul-Alexis Dray, Suchitra Dubey, Tatiana Ekeinhor, Marco Di Giovanni, Tanya Goyal, Rishabh Gupta, Louanes Hamla, Sang Han, Fabrice Harel-Canada, Antoine Honoré, Ishan Jindal, Przemysław Joniak, Denis Kleyko, Venelin Kovatchev, Kalpesh Krishna, Ashutosh Kumar, Stefan Langer, Seungjae Ryan Lee, Corey James Levinson, Hualou Liang, Kaizhao Liang, Zhexiong Liu, Andrey Lukyanenko, Vukosi Marivate, Gerard De Melo, Simon Meoni, Maxine Meyer, Afnan Mir, Nafise Sadat Moosavi, Niklas Meunnighoff, Timothy Sum Hon Mun, Kenton Murray, Marcin Namysl, Maria Obedkova, Priti Oli, Nivranshu Pasricha, Jan Pfister, Richard Plant, Vinay Prabhu, Vasile Pais, Libo Qin, Shahab Raji, Pawan Kumar Rajpoot, Vikas Raunak, Roy Rinberg, Nicholas Roberts, Juan Diego Rodriguez, Claude Roux, Vasconcellos Samus, Ananya Sai, Robin Schmidt, Thomas Scialom, Tshephisho Sefara, Saqib Shamsi, Xudong Shen, Yiwen Shi, Haoyue Shi, Anna Shvets, Nick Siegel, Damien Sileo, Jamie Simon, Chandan Singh, Roman Sitelew, Priyank Soni, Taylor Sorensen, William Soto, Aman Srivastava, Aditya Srivatsa, Tony Sun, Mukund Varma, A Tabassum, Fiona Tan, Ryan Teehan, Mo Tiwari, Marie Tolkiehn, Athena Wang, Zijian Wang, Zijie Wang, Gloria Wang, Fuxuan Wei, Bryan Wilie, Genta Indra Winata, Xinyu Wu, Witold Wydmanski, Tianbao Xie, Usama Yaseen, Michael Yee, Jing Zhang, Yue Zhang
Publikováno v:
Northern European Journal of Language Technology. 9
Data augmentation is an important method for evaluating the robustness of and enhancing the diversity of training data for natural language processing (NLP) models. In this paper, we present NL-Augmenter, a new participatory Python-based natural lang
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e507b440433920e9d41b1179362c83ba
https://doi.org/10.3384/nejlt.2000-1533.2023.4725
https://doi.org/10.3384/nejlt.2000-1533.2023.4725
Publikováno v:
NAACL-HLT
The task of long-form question answering (LFQA) involves retrieving documents relevant to a given question and using them to generate a paragraph-length answer. While many models have recently been proposed for LFQA, we show in this paper that the ta
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9c64ce740f8e2fad12c514d2e766705
http://arxiv.org/abs/2103.06332
http://arxiv.org/abs/2103.06332
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783030721121
ECIR (1)
ECIR (1)
Recent studies on Question Answering (QA) and Conversational QA (ConvQA) emphasize the role of retrieval: a system first retrieves evidence from a large collection and then extracts answers. This open-retrieval ConvQA setting typically assumes that e
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::42ec2fb60e943d8d7d9537a871b71419
https://doi.org/10.1007/978-3-030-72113-8_35
https://doi.org/10.1007/978-3-030-72113-8_35
Autor:
Pavitra Dangati, Ahsaas Bajaj, Pradhiksha Ashok Kumar, Rajarshi Das, Dominic Dotterrer, Kalpesh Krishna, Eliot Brenner, Rheeya Uppaal, Bradford Windsor, Andrew McCallum
Publikováno v:
ACL (student)
ive summarization is the task of compressing a long document into a coherent short document while retaining salient information. Modern abstractive summarization methods are based on deep neural networks which often require large training datasets. S
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fd90f891f526231dd4ab990e70cb7257
https://doi.org/10.18653/v1/2021.acl-srw.7
https://doi.org/10.18653/v1/2021.acl-srw.7
Publikováno v:
EMNLP (1)
Modern NLP defines the task of style transfer as modifying the style of a given sentence without appreciably changing its semantics, which implies that the outputs of style transfer systems should be paraphrases of their inputs. However, many existin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aaf3ed1e2297f1b82efdcbfc13ffb042
Autor:
Kalpesh Krishna, Mohit Iyyer
Publikováno v:
ACL (1)
The process of knowledge acquisition can be viewed as a question-answer game between a student and a teacher in which the student typically starts by asking broad, open-ended questions before drilling down into specifics (Hintikka, 1981; Hakkarainen
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::db77736ff270d73512f65a73387c2cf8
http://arxiv.org/abs/1906.02622
http://arxiv.org/abs/1906.02622
Publikováno v:
ACL (1)
Standard decoders for neural machine translation autoregressively generate a single target token per time step, which slows inference especially for long outputs. While architectural advances such as the Transformer fully parallelize the decoder comp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ed0a71611c7c6170a8e59aebbfe15e12
Autor:
Matt Earnshaw, Manas Mangaonkar, Arthur Eigenbrot, Abhigyan Bose, Ankit Agrawal, Juan Camilo Buitrago-Casas, Thomas P. Robitaille, Brigitta Sipőcz, Rishabh Sharma, Yash Kothari, Jordan Ballew, Shresth Verma, Jack Ireland, Alex Hamilton, Ole Streicher, Sarthak Jain, Tiago M. D. Pereira, Yudhik Agrawal, John G Evans, David Pérez-Suárez, S. Dacie, Michael Charlton, Agneet Chatterjee, Russell J. Hewett, Samuel Bennett, Ambar Mehrotra, Steven Christe, Ishtyaq Habib, Daniel D'Avella, Florian Mayer, Sanjeev Dubey, Igor Babuschkin, Chloé Guennou, Carlos Molina, Punyaslok Pattnaik, Freek Verstringe, Jose Ivan Campos Rozo, Kaustubh Hiware, Deepankar Sharma, Shane A. Maloney, Tomas Meszaros, Tessa D. Wilkinson, Tannmay Yadav, N. Freij, Swapnil Sharma, W. T. Barnes, Mateo Inchaurrandieta, Sophie A. Murray, Emmanuel Arias, Andrew J. Leonard, Stuart Mumford, Harsh Mathur, Rajasekhar Reddy Mekala, Nicky Chorley, James Mason, Rishabh Mishra, Dhruv Goel, Juanjo Bazán, Goran Cetusic, Nicholas A. Murphy, Michael Malocha, Arseniy Kustov, Sanskar Modi, Shashank Srikanth, S. Zahniy, Vishnunarayan K, Himanshu, Jacob, Duygu Keşkek, Norbert G. Gyenge, Andrew Hill, Pritish Chakraborty, Daniel Williams, Jaylen Wimbish, Ankit Baruah, André Chicrala, Ankit Kumar, Michael Mueller, Airmansmith, Garrison Taylor, Rajul Srivastava, David Stansby, Adrian M. Price-Whelan, Asish Panda, Mickaël Schoentgen, Ruben De Visscher, Yasintoda, Yash Krishan, Quinn Arbolante, Matt Bates, Sourav Ghosh, Reid Gomillion, Nitin Choudhary, Simon Liedtke, Monica G. Bobra, Daniel Ryan, Bernhard M. Wiedemann, Fionnlagh Mackenzie Dover, Abigail L. Stevens, Erik M. Bray, Albert Y. Shih, Brandon Stone, Dominik Stańczak, Naman, Jongyeob Park, Gulshan Kumar, Joseph Letts, Priyank Lodha, Dumindu Buddhika, Sudarshan Konge, Ankit, Larry Manley, Trung Kien Dang, V. Keith Hughitt, Akramul Haque, Michael S. Kirk, Jamescalixto, Matthew Mendero, Benjamin Mampaey, Laura A. Hayes, Yash Jain, Andrew Inglis, Prateek Chanda, Yash Sharma, Kalpesh Krishna, Jai Ram Rideout
Publikováno v:
Journal of Open Source Software. 5:1832
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::410570729b4c07326fd3ce821a1a89f7
https://doi.org/10.21105/joss.01832
https://doi.org/10.21105/joss.01832
Publikováno v:
EMNLP
We analyze the performance of different sentiment classification models on syntactically complex inputs like A-but-B sentences. The first contribution of this analysis addresses reproducible research: to meaningfully compare different models, their a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5784c2300c4ca59fd84bc2ec51d9824
Publikováno v:
ICASSP
Connectionist temporal classification (CTC) is a popular sequence prediction approach for automatic speech recognition that is typically used with models based on recurrent neural networks (RNNs). We explore whether deep convolutional neural networks
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c721d5c0eb7392cda02085b5e04d604f