In addition to transfer learning, the project will study multilingual machine translation, self-supervised machine translation and unsupervised machine translation.

Terminology translation plays a critical role in domain-specific machine translation (MT). Phrase-based statistical MT (PB-SMT) has been the dominant approach to MT for the past 30 years, both in ...

Most studies find that neural machine-translation models can translate only about 30 percent of novel excerpts—usually simple passages—with acceptable quality, as determined by native speakers.

Compared to traditional statistical machine translation (SMT), such as phrase-based machine translation (PBMT), neural machine translation (NMT) often sacrifices adequacy for the sake of fluency.

Lingua Marina discusses the possibility of language learning becoming obsolete due to the rise of AI neural networks that can ...

Translation Software Market Will Likely Total USD 10.7 Billion In 2025, Expected To Rise To USD 20.1 Billion By 2035, Rising ...

Researchers at the University of Pennsylvania have developed a programmable photonic chip. This innovation could transform artificial intelligence's machine learning by using light ...