  Many multilingual NLP applications need to translate words between different
languages, but cannot afford the computational expense of inducing or applying
a full translation model. For these applications, we have designed a fast
algorithm for estimating a partial translation model, which accounts for
translational equivalence only at the word level. The model's precision/recall
trade-off can be directly controlled via one threshold parameter. This feature
makes the model more suitable for applications that are not fully statistical.
The model's hidden parameters can be easily conditioned on information
extrinsic to the model, providing an easy way to integrate pre-existing
knowledge such as part-of-speech, dictionaries, word order, etc.. Our model can
link word tokens in parallel texts as well as other translation models in the
literature. Unlike other translation models, it can automatically produce
dictionary-sized translation lexicons, and it can do so with over 99% accuracy.
