Statistical machine translation (SMT) is a method of automatic translation that uses mathematical models to translate text from one language to another. This tutorial will provide an overview of SMT, its key components, and how it works.

Key Components of SMT

  1. Corpus: A large collection of bilingual text that serves as the training data for the translation model.
  2. Lexicon: A database of word pairs between the source and target languages.
  3. Translation Model: A statistical model that predicts the probability of a sequence of words in the target language given a sequence of words in the source language.
  4. Reordering Model: A model that predicts the order of words in the target language to ensure grammatical correctness.

How SMT Works

  1. Preprocessing: The input text is preprocessed to remove punctuation, convert to lowercase, and tokenize into words.
  2. Translation: The source text is translated into the target language using the SMT model.
  3. Postprocessing: The translated text is postprocessed to correct grammatical errors and improve readability.

Example

Here's an example of how SMT works:

Source Text: "How are you today?" Target Text: "今天你好吗？"

Learn More

To learn more about SMT and its applications, check out our comprehensive guide on Machine Translation.

Translation Model