IBM Statistical Machine Translation (1991) › Translation in Practice: From Words to Meaning

To understand how the IBM models worked in practice, consider the seemingly simple task of translating "I love cats" to French. The target translation is "J'aime les chats," but the word-to-word correspondence reveals the complexity hidden in even basic sentences.

The English pronoun "I" becomes "J'" in French, a contracted form of "je" that merges with the following verb. The verb "love" aligns with "aime," the first person singular form of "aimer." But "cats" presents an interesting case: it aligns with both "les" and "chats," the article and noun together. This demonstrates the fertility concept from Model 3, where one English word generates multiple French words.

What makes this example revealing is what the model must learn implicitly. English allows bare plural nouns like "cats" to express generic meaning, while French requires the definite article "les." The model doesn't learn this as an explicit grammatical rule, but rather as a statistical pattern: when translating generic plural nouns from English to French, the presence of an article in French is highly probable. Similarly, the model learns that French verb forms must agree with their subjects, that pronouns contract before vowels, and countless other patterns, all emerging from observed correspondences in the training data.