Paper: A Word-To-Word Model Of Translational Equivalence

ACL ID P97-1063
Title A Word-To-Word Model Of Translational Equivalence
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 1997

Many multilingual NLP applications need to translate words between different lan- guages, but cannot afford the computa- tional expense of inducing or applying a full translation model. For these applications, we have designed a fast algorithm for esti- mating a partial translation model, which accounts for translational equivalence only at the word level. The model's preci- sion/recall trade-off can be directly con- trolled 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 eas- ily conditioned on information extrinsic to the model, providing an easy way to inte- grate pre-existing knowledge such as part- of-speech, dictionaries, word order, etc.. Our model can link word tokens in pa...