Zobrazeno 1 - 10
of 29
pro vyhledávání: '"Lakew, Surafel"'
Autor:
Chronopoulou, Alexandra, Thompson, Brian, Mathur, Prashant, Virkar, Yogesh, Lakew, Surafel M., Federico, Marcello
Automatic dubbing (AD) is the task of translating the original speech in a video into target language speech. The new target language speech should satisfy isochrony; that is, the new speech should be time aligned with the original video, including m
Externí odkaz:
http://arxiv.org/abs/2302.12979
Autor:
Hieber, Felix, Denkowski, Michael, Domhan, Tobias, Barros, Barbara Darques, Ye, Celina Dong, Niu, Xing, Hoang, Cuong, Tran, Ke, Hsu, Benjamin, Nadejde, Maria, Lakew, Surafel, Mathur, Prashant, Currey, Anna, Federico, Marcello
Sockeye 3 is the latest version of the Sockeye toolkit for Neural Machine Translation (NMT). Now based on PyTorch, Sockeye 3 provides faster model implementations and more advanced features with a further streamlined codebase. This enables broader ex
Externí odkaz:
http://arxiv.org/abs/2207.05851
Automatic dubbing (AD) is among the machine translation (MT) use cases where translations should match a given length to allow for synchronicity between source and target speech. For neural MT, generating translations of length close to the source le
Externí odkaz:
http://arxiv.org/abs/2112.08682
We introduce the task of isochrony-aware machine translation which aims at generating translations suitable for dubbing. Dubbing of a spoken sentence requires transferring the content as well as the speech-pause structure of the source into the targe
Externí odkaz:
http://arxiv.org/abs/2112.08548
Autor:
Lakew, Surafel M., Federico, Marcello, Wang, Yue, Hoang, Cuong, Virkar, Yogesh, Barra-Chicote, Roberto, Enyedi, Robert
Automatic dubbing aims at seamlessly replacing the speech in a video document with synthetic speech in a different language. The task implies many challenges, one of which is generating translations that not only convey the original content, but also
Externí odkaz:
http://arxiv.org/abs/2110.03847
Autor:
Lakew, Surafel Melaku
Machine Translation (MT) is the task of mapping a source language to a target language. The recent introduction of neural MT (NMT) has shown promising results for high-resource language, however, poorly performing for low-resource language (LRL) sett
Externí odkaz:
http://hdl.handle.net/11572/257906
Neural Machine Translation (NMT) approaches employing monolingual data are showing steady improvements in resource rich conditions. However, evaluations using real-world low-resource languages still result in unsatisfactory performance. This work pro
Externí odkaz:
http://arxiv.org/abs/2103.05951
Recent advents in Neural Machine Translation (NMT) have shown improvements in low-resource language (LRL) translation tasks. In this work, we benchmark NMT between English and five African LRL pairs (Swahili, Amharic, Tigrigna, Oromo, Somali [SATOS])
Externí odkaz:
http://arxiv.org/abs/2003.14402
Multilingual Neural Machine Translation (MNMT) for low-resource languages (LRL) can be enhanced by the presence of related high-resource languages (HRL), but the relatedness of HRL usually relies on predefined linguistic assumptions about language si
Externí odkaz:
http://arxiv.org/abs/1910.13998
The recent advances introduced by neural machine translation (NMT) are rapidly expanding the application fields of machine translation, as well as reshaping the quality level to be targeted. In particular, if translations have to fit some given layou
Externí odkaz:
http://arxiv.org/abs/1910.10408