Example-Based Machine Translation
Example-Based Machine Translation (EBMT) is a data-driven approach to machine translation that translates new sentences by retrieving and adapting similar translation examples from a bilingual corpus. It operates by breaking input sentences into fragments, finding matching examples in the corpus, and recombining them to produce translations, often using analogy and similarity-based reasoning. This methodology contrasts with rule-based or statistical neural methods by leveraging existing translated pairs directly.
Developers should learn EBMT when working on translation systems for languages with limited parallel data, as it can be effective with smaller corpora compared to deep learning models. It is particularly useful for domain-specific translations (e.g., technical manuals) where example-based adaptation yields high accuracy, and in scenarios requiring interpretability, as translations are derived from concrete examples rather than opaque neural networks.