Machine Translation

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Data Portraits: Recording Foundation Model Training Data (2023, background): “Some target text is translated to English specifically for this resource and should not appear naturally in a web-scrape absent leakage.”
Experiments and Prospects of Example-Based Machine Translation (1991, background): “Machine Translation requires handcmt~ and complicated large-scale knowledge (Nirenburg 1987).”
Artificial Intelligence and the Public Sector—Applications and Challenges (2018, methodology): “…language ● May include real-time universal translation and natural language processing systems (Pannu 2015) ● Real-time universal translation (Microsoft 2018) to translate speech and text in face-to-face communications in public service settings ● Administrative workflow assistance with…”
Understanding the societal impacts of machine translation: a critical review of the literature on medical and legal use cases (2020, unknown): “While this avenue was not pursued in this case, this relates to the fact that the very use of online MT can break con ﬁ dentiality, for instance because the content may be shared with the MT provider (Kenny, 2019).”
Transforming machine translation: a deep learning system reaches news translation quality comparable to human professionals (2020, background): “Among key complications is the rich morphology in the source and especially in the target language 2 .”