Zobrazeno 1 - 10
of 2 761
pro vyhledávání: '"Barrault A"'
Autor:
LCM team, Barrault, Loïc, Duquenne, Paul-Ambroise, Elbayad, Maha, Kozhevnikov, Artyom, Alastruey, Belen, Andrews, Pierre, Coria, Mariano, Couairon, Guillaume, Costa-jussà, Marta R., Dale, David, Elsahar, Hady, Heffernan, Kevin, Janeiro, João Maria, Tran, Tuan, Ropers, Christophe, Sánchez, Eduardo, Roman, Robin San, Mourachko, Alexandre, Saleem, Safiyyah, Schwenk, Holger
LLMs have revolutionized the field of artificial intelligence and have emerged as the de-facto tool for many tasks. The current established technology of LLMs is to process input and generate output at the token level. This is in sharp contrast to hu
Externí odkaz:
http://arxiv.org/abs/2412.08821
The presence of dips in the gravity modes period spacing versus period diagram of gamma Doradus stars is now well established by recent asteroseismic studies. Such Lorentzian-shaped inertial dips arise from the interaction of gravito-inertial modes i
Externí odkaz:
http://arxiv.org/abs/2412.02849
The presence of dips in the gravito-inertial modes period-spacing pattern of gamma-Dor stars is now well established by recent asteroseismic studies. Such Lorentzian-shaped inertial dips arise from the interaction of gravito-inertial modes propagatin
Externí odkaz:
http://arxiv.org/abs/2410.21425
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