Machine Translation with Inferred Stochastic Finite-State Transducers Export

@article{citeulike:4510064, abstract = {Finite-state transducers are models that are being used in different areas of pattern recognition and computational linguistics. One of these areas is machine translation, in which the approaches that are based on building models automatically from training examples are becoming more and more attractive. Finite-state transducers are very adequate for use in constrained tasks in which training samples of pairs of sentences are available. A technique for inferring finite-state transducers is proposed in this article. This technique is based on formal relations between finite-state transducers and rational grammars. Given a training corpus of source-target pairs of sentences, the proposed approach uses statistical alignment methods to produce a set of conventional strings from which a stochastic rational grammar (e.g., an n -gram) is inferred. This grammar is finally converted into a finite-state transducer. The proposed methods are assessed through a series of machine translation experiments within the framework of the EuTrans project.}, address = {Cambridge, MA, USA}, author = {Casacuberta, Francisco and Vidal, Enrique}, citeulike-article-id = {4510064}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1105714}, citeulike-linkout-1 = {http://dx.doi.org/10.1162/089120104323093294}, doi = {10.1162/089120104323093294}, issn = {0891-2017}, journal = {Comput. Linguist.}, keywords = {finite-state, smt}, number = {2}, pages = {205--225}, posted-at = {2009-08-12 16:58:45}, priority = {2}, publisher = {MIT Press}, title = {Machine Translation with Inferred Stochastic Finite-State Transducers}, url = {http://dx.doi.org/10.1162/089120104323093294}, volume = {30}, year = {2004} }

TY - JOUR ID - citeulike:4510064 L3 - citeulike-article-id:4510064 N2 - Finite-state transducers are models that are being used in different areas of pattern recognition and computational linguistics. One of these areas is machine translation, in which the approaches that are based on building models automatically from training examples are becoming more and more attractive. Finite-state transducers are very adequate for use in constrained tasks in which training samples of pairs of sentences are available. A technique for inferring finite-state transducers is proposed in this article. This technique is based on formal relations between finite-state transducers and rational grammars. Given a training corpus of source-target pairs of sentences, the proposed approach uses statistical alignment methods to produce a set of conventional strings from which a stochastic rational grammar (e.g., an n -gram) is inferred. This grammar is finally converted into a finite-state transducer. The proposed methods are assessed through a series of machine translation experiments within the framework of the EuTrans project. IS - 2 JF - Comput. Linguist. SN - 0891-2017 AD - Cambridge, MA, USA EP - 225 TI - Machine Translation with Inferred Stochastic Finite-State Transducers PB - MIT Press VL - 30 SP - 205 KW - finite-state KW - smt AU - Casacuberta, Francisco AU - Vidal, Enrique PY - 2004/// UR - http://dx.doi.org/10.1162/089120104323093294 ER -