Zobrazeno 1 - 10
of 15
pro vyhledávání: '"Ropers, Christophe"'
Autor:
Tan, Xiaoqing Ellen, Hansanti, Prangthip, Wood, Carleigh, Yu, Bokai, Ropers, Christophe, Costa-jussà, Marta R.
In the current landscape of automatic language generation, there is a need to understand, evaluate, and mitigate demographic biases as existing models are becoming increasingly multilingual. To address this, we present the initial eight languages fro
Externí odkaz:
http://arxiv.org/abs/2407.00486
Autor:
Ropers, Christophe, Dale, David, Hansanti, Prangthip, Gonzalez, Gabriel Mejia, Evtimov, Ivan, Wong, Corinne, Touret, Christophe, Pereyra, Kristina, Kim, Seohyun Sonia, Ferrer, Cristian Canton, Andrews, Pierre, Costa-jussà, Marta R.
Assessing performance in Natural Language Processing is becoming increasingly complex. One particular challenge is the potential for evaluation datasets to overlap with training data, either directly or indirectly, which can lead to skewed results an
Externí odkaz:
http://arxiv.org/abs/2401.16247
Autor:
Costa-jussà, Marta R., Meglioli, Mariano Coria, Andrews, Pierre, Dale, David, Hansanti, Prangthip, Kalbassi, Elahe, Mourachko, Alex, Ropers, Christophe, Wood, Carleigh
Research in toxicity detection in natural language processing for the speech modality (audio-based) is quite limited, particularly for languages other than English. To address these limitations and lay the groundwork for truly multilingual audio-base
Externí odkaz:
http://arxiv.org/abs/2401.05060
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:
Muller, Benjamin, Alastruey, Belen, Hansanti, Prangthip, Kalbassi, Elahe, Ropers, Christophe, Smith, Eric Michael, Williams, Adina, Zettlemoyer, Luke, Andrews, Pierre, Costa-jussà, Marta R.
Gender biases in language generation systems are challenging to mitigate. One possible source for these biases is gender representation disparities in the training and evaluation data. Despite recent progress in documenting this problem and many atte
Externí odkaz:
http://arxiv.org/abs/2308.16871
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:
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:
Costa-jussà, Marta R., Smith, Eric, Ropers, Christophe, Licht, Daniel, Maillard, Jean, Ferrando, Javier, Escolano, Carlos
Machine Translation systems can produce different types of errors, some of which are characterized as critical or catastrophic due to the specific negative impact that they can have on users. In this paper we focus on one type of critical error: adde
Externí odkaz:
http://arxiv.org/abs/2210.03070
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