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2018 BCE
Common Challenges for Human and Machine Translation
Since the year 2000, progress has been made in the implementation of translation models for machines, which would allow translation processes of texts, voice and video, these advances have been made by the investment in technology that has been put at the service of the language -
2018 BCE
Common Challenges for Human and Machine Translation
The challenges that researchers raised when creating programs or algorithms that would improve the translation experience, compared to the version that a person could give, are focused on the correction of the versions and the comparison that could make the difference between the man and the machine. -
2018 BCE
The mechanics of translators
The job of creating or improving translation programs is that the machine does not have the capacity to contextualize, taking into account the information provided by the narrator, the text, or the audio visual material, in this version of the story the machine is seen at a disadvantage to compete with the human translator. -
2018 BCE
The mechanics of translators
Advances in technology and data storage capacity, are now giving the machine an advantage over humans, although it can not form context, based on information from the source, what the machine does is to create statistical models of small translations, that allow you, through complex algorithms, to bring your level of translation closer to that of the human, through repetitive models, today the Whats app is the best example. -
2018 BCE
Context-Based Machine Translation
Translation based on contexts, sometimes it may sound impossible but, reality says quite the opposite, many of the best programmers and linguists in the world, are working to create what is called as a contextual translator, a large database of words and examples of use with location, space, feeling and others, could frame contexts to achieve a result in the final version of the translation that resembles a human translation.
