Zobrazeno 1 - 10
of 40
pro vyhledávání: '"Gao, Cynthia"'
Autor:
Communication, Seamless, Barrault, Loïc, Chung, Yu-An, Meglioli, Mariano Coria, Dale, David, Dong, Ning, Duppenthaler, Mark, Duquenne, Paul-Ambroise, Ellis, Brian, Elsahar, Hady, Haaheim, Justin, Hoffman, John, Hwang, Min-Jae, Inaguma, Hirofumi, Klaiber, Christopher, Kulikov, Ilia, Li, Pengwei, Licht, Daniel, Maillard, Jean, Mavlyutov, Ruslan, Rakotoarison, Alice, Sadagopan, Kaushik Ram, Ramakrishnan, Abinesh, Tran, Tuan, Wenzek, Guillaume, Yang, Yilin, Ye, Ethan, Evtimov, Ivan, Fernandez, Pierre, Gao, Cynthia, Hansanti, Prangthip, Kalbassi, Elahe, Kallet, Amanda, Kozhevnikov, Artyom, Gonzalez, Gabriel Mejia, Roman, Robin San, Touret, Christophe, Wong, Corinne, Wood, Carleigh, Yu, Bokai, Andrews, Pierre, Balioglu, Can, Chen, Peng-Jen, Costa-jussà, Marta R., Elbayad, Maha, Gong, Hongyu, Guzmán, Francisco, Heffernan, Kevin, Jain, Somya, Kao, Justine, Lee, Ann, Ma, Xutai, Mourachko, Alex, Peloquin, Benjamin, Pino, Juan, Popuri, Sravya, Ropers, Christophe, Saleem, Safiyyah, Schwenk, Holger, Sun, Anna, Tomasello, Paden, Wang, Changhan, Wang, Jeff, Wang, Skyler, Williamson, Mary
Large-scale automatic speech translation systems today lack key features that help machine-mediated communication feel seamless when compared to human-to-human dialogue. In this work, we introduce a family of models that enable end-to-end expressive
Externí odkaz:
http://arxiv.org/abs/2312.05187
Autor:
Communication, Seamless, Barrault, Loïc, Chung, Yu-An, Meglioli, Mariano Cora, Dale, David, Dong, Ning, Duquenne, Paul-Ambroise, Elsahar, Hady, Gong, Hongyu, Heffernan, Kevin, Hoffman, John, Klaiber, Christopher, Li, Pengwei, Licht, Daniel, Maillard, Jean, Rakotoarison, Alice, Sadagopan, Kaushik Ram, Wenzek, Guillaume, Ye, Ethan, Akula, Bapi, Chen, Peng-Jen, Hachem, Naji El, Ellis, Brian, Gonzalez, Gabriel Mejia, Haaheim, Justin, Hansanti, Prangthip, Howes, Russ, Huang, Bernie, Hwang, Min-Jae, Inaguma, Hirofumi, Jain, Somya, Kalbassi, Elahe, Kallet, Amanda, Kulikov, Ilia, Lam, Janice, Li, Daniel, Ma, Xutai, Mavlyutov, Ruslan, Peloquin, Benjamin, Ramadan, Mohamed, Ramakrishnan, Abinesh, Sun, Anna, Tran, Kevin, Tran, Tuan, Tufanov, Igor, Vogeti, Vish, Wood, Carleigh, Yang, Yilin, Yu, Bokai, Andrews, Pierre, Balioglu, Can, Costa-jussà, Marta R., Celebi, Onur, Elbayad, Maha, Gao, Cynthia, Guzmán, Francisco, Kao, Justine, Lee, Ann, Mourachko, Alexandre, Pino, Juan, Popuri, Sravya, Ropers, Christophe, Saleem, Safiyyah, Schwenk, Holger, Tomasello, Paden, Wang, Changhan, Wang, Jeff, Wang, Skyler
What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed machine translation coverage beyond 200 languages, unified speech-to-
Externí odkaz:
http://arxiv.org/abs/2308.11596
Autor:
Touvron, Hugo, Martin, Louis, Stone, Kevin, Albert, Peter, Almahairi, Amjad, Babaei, Yasmine, Bashlykov, Nikolay, Batra, Soumya, Bhargava, Prajjwal, Bhosale, Shruti, Bikel, Dan, Blecher, Lukas, Ferrer, Cristian Canton, Chen, Moya, Cucurull, Guillem, Esiobu, David, Fernandes, Jude, Fu, Jeremy, Fu, Wenyin, Fuller, Brian, Gao, Cynthia, Goswami, Vedanuj, Goyal, Naman, Hartshorn, Anthony, Hosseini, Saghar, Hou, Rui, Inan, Hakan, Kardas, Marcin, Kerkez, Viktor, Khabsa, Madian, Kloumann, Isabel, Korenev, Artem, Koura, Punit Singh, Lachaux, Marie-Anne, Lavril, Thibaut, Lee, Jenya, Liskovich, Diana, Lu, Yinghai, Mao, Yuning, Martinet, Xavier, Mihaylov, Todor, Mishra, Pushkar, Molybog, Igor, Nie, Yixin, Poulton, Andrew, Reizenstein, Jeremy, Rungta, Rashi, Saladi, Kalyan, Schelten, Alan, Silva, Ruan, Smith, Eric Michael, Subramanian, Ranjan, Tan, Xiaoqing Ellen, Tang, Binh, Taylor, Ross, Williams, Adina, Kuan, Jian Xiang, Xu, Puxin, Yan, Zheng, Zarov, Iliyan, Zhang, Yuchen, Fan, Angela, Kambadur, Melanie, Narang, Sharan, Rodriguez, Aurelien, Stojnic, Robert, Edunov, Sergey, Scialom, Thomas
In this work, we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use ca
Externí odkaz:
http://arxiv.org/abs/2307.09288
Autor:
Costa-jussà, Marta R., Andrews, Pierre, Smith, Eric, Hansanti, Prangthip, Ropers, Christophe, Kalbassi, Elahe, Gao, Cynthia, Licht, Daniel, Wood, Carleigh
We introduce a multilingual extension of the HOLISTICBIAS dataset, the largest English template-based taxonomy of textual people references: MULTILINGUALHOLISTICBIAS. This extension consists of 20,459 sentences in 50 languages distributed across all
Externí odkaz:
http://arxiv.org/abs/2305.13198
Autor:
Dale, David, Voita, Elena, Lam, Janice, Hansanti, Prangthip, Ropers, Christophe, Kalbassi, Elahe, Gao, Cynthia, Barrault, Loïc, Costa-jussà, Marta R.
Publikováno v:
EMNLP 2023
Hallucinations in machine translation are translations that contain information completely unrelated to the input. Omissions are translations that do not include some of the input information. While both cases tend to be catastrophic errors undermini
Externí odkaz:
http://arxiv.org/abs/2305.11746
Autor:
NLLB Team, Costa-jussà, Marta R., Cross, James, Çelebi, Onur, Elbayad, Maha, Heafield, Kenneth, Heffernan, Kevin, Kalbassi, Elahe, Lam, Janice, Licht, Daniel, Maillard, Jean, Sun, Anna, Wang, Skyler, Wenzek, Guillaume, Youngblood, Al, Akula, Bapi, Barrault, Loic, Gonzalez, Gabriel Mejia, Hansanti, Prangthip, Hoffman, John, Jarrett, Semarley, Sadagopan, Kaushik Ram, Rowe, Dirk, Spruit, Shannon, Tran, Chau, Andrews, Pierre, Ayan, Necip Fazil, Bhosale, Shruti, Edunov, Sergey, Fan, Angela, Gao, Cynthia, Goswami, Vedanuj, Guzmán, Francisco, Koehn, Philipp, Mourachko, Alexandre, Ropers, Christophe, Saleem, Safiyyah, Schwenk, Holger, Wang, Jeff
Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However, such efforts have coalesced around a small subset of languages, leavin
Externí odkaz:
http://arxiv.org/abs/2207.04672
Obtaining meaningful quality scores for machine translation systems through human evaluation remains a challenge given the high variability between human evaluators, partly due to subjective expectations for translation quality for different language
Externí odkaz:
http://arxiv.org/abs/2205.08533
Autor:
Goyal, Naman, Gao, Cynthia, Chaudhary, Vishrav, Chen, Peng-Jen, Wenzek, Guillaume, Ju, Da, Krishnan, Sanjana, Ranzato, Marc'Aurelio, Guzman, Francisco, Fan, Angela
One of the biggest challenges hindering progress in low-resource and multilingual machine translation is the lack of good evaluation benchmarks. Current evaluation benchmarks either lack good coverage of low-resource languages, consider only restrict
Externí odkaz:
http://arxiv.org/abs/2106.03193
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