Application of autoregressive generation given a prefix: Machine translation
The data used to train the model are known as parallel texts, or bitexts. bitexts = source + + target , where source is the text being translated, target is the translation output, and is the end-of-sentence token.
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Data Science
Related
Encoder-decoder networks
Backtranslation
MT Evaluation
MT Corpora
Assessing Translation Effectiveness for a Specific Use Case
A company is developing a translation service for legal documents, where preserving the precise meaning and complex sentence structure of the original text is the highest priority. The company has access to a massive parallel corpus of legal texts. Given these requirements, which approach would be more suitable and why?
Evaluating Machine Translation Quality
Unaligned Data in Sequence Learning
Statistical Machine Translation (SMT)
Statistical Machine Translation vs Neural Machine Translation
Related
Encoder-decoder networks
Backtranslation
MT Evaluation
MT Corpora
Assessing Translation Effectiveness for a Specific Use Case
A company is developing a translation service for legal documents, where preserving the precise meaning and complex sentence structure of the original text is the highest priority. The company has access to a massive parallel corpus of legal texts. Given these requirements, which approach would be more suitable and why?
Evaluating Machine Translation Quality
Unaligned Data in Sequence Learning
Statistical Machine Translation (SMT)
Statistical Machine Translation vs Neural Machine Translation