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The process of making corrective changes to a translated text produced by a machine translation system, typically done by a human translator.

A method where a text is translated from a source language to a target language, and then back to the source language by a different system or manually to check translation accuracy.

The process by which a machine translation system is adapted to better perform on a specific subject or field, often by using a specialized corpus.

A component of a machine translation system that generates the target language translation from a model's representations.

Bilingual Evaluation Understudy, a metric for evaluating the quality of text which has been machine-translated from one natural language to another.

A component in neural machine translation models that allows the model to focus on different parts of the input sequence when generating each word of the output sequence.

Standards or measures used to assess the quality and performance of machine translation outputs, such as BLEU, METEOR, and TER.

The process of using automated software to translate text or speech from one language to another.

The process of dividing a sequence of textual material into smaller parts called tokens, such as words or phrases, often as a first step in machine translation.

A large and structured set of texts used in machine translation for statistical analysis and hypothesis testing.

Statistical Machine Translation, a machine translation technique that uses statistical models to generate translations based on analysis of bilingual text corpora.

A type of statistical machine translation where translations are based on phrases (chunks of words) rather than individual words.

A component of a machine translation system that converts the source language input into a meaningful, fixed-sized representation.

Neural Machine Translation, a machine translation approach that uses a large neural network capable of learning to translate between languages from large datasets.