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
Authors

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...