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IBM alignment models
The IBM alignment models are a sequence of increasingly complex models used in statistical machine translation to train a translation model and an alignment model, starting with lexical translation probabilities and moving to reordering and word duplication. They underpinned the majority of statistical machine translation systems for almost twenty years starting in the early 1990s, until neural machine translation began to dominate. These models offer principled probabilistic formulation and (mostly) tractable inference.
The IBM alignment models were published in parts in 1988 and 1990, and the entire series is published in 1993. Every author of the 1993 paper subsequently went to the hedge fund Renaissance Technologies.
The original work on statistical machine translation at IBM proposed five models, and a model 6 was proposed later. The sequence of the six models can be summarized as:
The IBM alignment models translation as a conditional probability model. For each source-language ("foreign") sentence , we generate both a target-language ("English") sentence and an alignment . The problem then is to find a good statistical model for , the probability that we would generate English language sentence and an alignment given a foreign sentence .
The meaning of an alignment grows increasingly complicated as the model version number grew. See Model 1 for the most simple and understandable version.
Given any foreign-English sentence pair , an alignment for the sentence pair is a function of type . That is, we assume that the English word at location is "explained" by the foreign word at location . For example, consider the following pair of sentences
It will surely rain tomorrow -- 明日 は きっと 雨 だ
We can align some English words to corresponding Japanese words, but not everyone:
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IBM alignment models
The IBM alignment models are a sequence of increasingly complex models used in statistical machine translation to train a translation model and an alignment model, starting with lexical translation probabilities and moving to reordering and word duplication. They underpinned the majority of statistical machine translation systems for almost twenty years starting in the early 1990s, until neural machine translation began to dominate. These models offer principled probabilistic formulation and (mostly) tractable inference.
The IBM alignment models were published in parts in 1988 and 1990, and the entire series is published in 1993. Every author of the 1993 paper subsequently went to the hedge fund Renaissance Technologies.
The original work on statistical machine translation at IBM proposed five models, and a model 6 was proposed later. The sequence of the six models can be summarized as:
The IBM alignment models translation as a conditional probability model. For each source-language ("foreign") sentence , we generate both a target-language ("English") sentence and an alignment . The problem then is to find a good statistical model for , the probability that we would generate English language sentence and an alignment given a foreign sentence .
The meaning of an alignment grows increasingly complicated as the model version number grew. See Model 1 for the most simple and understandable version.
Given any foreign-English sentence pair , an alignment for the sentence pair is a function of type . That is, we assume that the English word at location is "explained" by the foreign word at location . For example, consider the following pair of sentences
It will surely rain tomorrow -- 明日 は きっと 雨 だ
We can align some English words to corresponding Japanese words, but not everyone: