Machine Translation

Translating text automatically from one language to another, the field that drove statistical NLP through word-alignment models and now relies on neural sequence-to-sequence translation.

Definition

Machine translation is the automatic conversion of text or speech from a source language into an equivalent expression in a target language.

Scope

Covers automatic translation between languages: word- and phrase-based statistical models, alignment and the noisy-channel framework, neural machine translation with attention and transformers, and the evaluation of translation quality. It addresses low-resource and multilingual translation. The general transformer architecture is covered in a sibling topic.

Core questions

• How does the noisy-channel model frame translation as a search problem?
• How are word and phrase alignments learned from parallel corpora?
• How did neural machine translation surpass phrase-based systems?
• How is translation quality measured automatically and by humans?

Key concepts

• parallel corpus
• word alignment
• phrase-based translation
• noisy-channel model
• neural machine translation
• subword units
• BLEU
• low-resource translation

Key theories

Statistical word-alignment models

Brown and colleagues' IBM models that learn word correspondences from parallel text and frame translation probabilistically, founding statistical machine translation.

Neural machine translation

End-to-end encoder-decoder models with attention that translate without explicit alignment or phrase tables, using subword units to handle rare words.

History

After the disappointments of early rule-based systems, Brown and colleagues' 1993 IBM models launched statistical machine translation, refined into phrase-based systems documented by Koehn. Neural machine translation emerged around 2014–2016, quickly becoming the standard and powering widely used translation services.

Debates

Adequacy of automatic evaluation

Metrics like BLEU enabled rapid progress but correlate imperfectly with human judgments of fluency and adequacy, keeping human evaluation essential for high-stakes assessment.

Key figures

• Peter Brown
• Robert Mercer
• Philipp Koehn
• Rico Sennrich

Seminal works

• brown1993
• papineni2002
• sennrich2016

Frequently asked questions

Why was machine translation so important to NLP's history?

Translation provided clear objectives, abundant parallel data, and a hard problem that rewarded statistical and then neural methods, so advances in MT repeatedly drove progress across the wider field.