Zobrazeno 1 - 10
of 2 703
pro vyhledávání: '"Barrault, A."'
Current pre-trained cross-lingual sentence encoders approaches use sentence-level objectives only. This can lead to loss of information, especially for tokens, which then degrades the sentence representation. We propose MEXMA, a novel approach that i
Externí odkaz:
http://arxiv.org/abs/2409.12737
In Natural Language Processing (NLP) classification tasks such as topic categorisation and sentiment analysis, model generalizability is generally measured with standard metrics such as Accuracy, F-Measure, or AUC-ROC. The diversity of metrics, and t
Externí odkaz:
http://arxiv.org/abs/2401.03831
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:
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
We propose a novel RoBERTa-based model, RoPPT, which introduces a target-oriented parse tree structure in metaphor detection. Compared to existing models, RoPPT focuses on semantically relevant information and achieves the state-of-the-art on several
Externí odkaz:
http://arxiv.org/abs/2302.05611
In this paper, we propose FrameBERT, a RoBERTa-based model that can explicitly learn and incorporate FrameNet Embeddings for concept-level metaphor detection. FrameBERT not only achieves better or comparable performance to the state-of-the-art, but a
Externí odkaz:
http://arxiv.org/abs/2302.04834
While the problem of hallucinations in neural machine translation has long been recognized, so far the progress on its alleviation is very little. Indeed, recently it turned out that without artificially encouraging models to hallucinate, previously
Externí odkaz:
http://arxiv.org/abs/2212.08597
Publikováno v:
Microbiology Spectrum, Vol 12, Iss 10 (2024)
ABSTRACT Staphylococcus aureus is a major contributor to bacterial-associated mortality, owing to its exceptional adaptability across diverse environments. Iron is vital to most organisms but can be toxic in excess. To manage its intracellular iron,
Externí odkaz:
https://doaj.org/article/1203e0f65aa640e7b2fd53e917d1a2c5
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