Joining linguistic and statistical methods for Spanish-to-Basque speech translation

Abstract

To cite this version:Alicia Pérez, M. Inés Torres, Francisco Casacuberta. Joining linguistic and statistical methods for Spanish-to-Basque speech translation. Speech Communication, Elsevier : North-Holland, 2008, 50 (11-12) AbstractThe goal of this work is to develop a text and speech translation system from Spanish to Basque. This pair of languages shows quite odd characteristics as they differ extraordinarily in both morphology and syntax, thus, attractive challenges in machine translation are involved. Nevertheless, since both languages share official status in the Basque Country, the underlying motivation is not only academic but also practical.Finite-state transducers were adopted as basic translation models. The main contribution of this work involves the study of several techniques to improve probabilistic finite-state transducers by means of additional linguistic knowledge. Two methods to cope with both linguistics and statistics were proposed. The first one performed a morphological analysis in an attempt to benefit from atomic meaningful units when it comes to rendering the meaning from one language to the other. The second approach aimed at clustering words according to their syntactic role and used such phrases as translation unit. From the latter approach phrase-based finite-state transducers arose as a natural extension of classical ones.The models were assessed under a restricted domain task, very repetitive and with a small vocabulary. Experimental results shown that both morphological and syntactical approaches outperformed the baseline under different test sets and architectures for speech translation.

GREAT: open source software for statistical machine translation

“…This property was used to propose a method of inference of SFSTs based on the inference of stochastic finite-state automata (SFSAs) (Casacuberta and Vidal, 2004). This method, which has been widely used in SMT applications (Casacuberta and Vidal, 2007;Pérez et al, 2008;González and Casacuberta, 2009), is known as GIATI and is the training framework of GREAT.…”

“…GREAT is based on a bilingual language modelling approach for SMT, which is so far implemented for n-gram models based on the framework of stochastic finitestate transducers (SFSTs). The software offers room for other language models that future developers may want to incorporate, whether they are finite-state representable models (Mariño et al, 2006;Pérez et al, …”

Aprendizaje de transductores estocásticos de estados finitos y su aplicación en traducción automática.

“…Sin embargo, la metodología GIATI trata de combinar todas estas operaciones sobre un único modelo de traducción. Otros autores [PTC07,PTC08] también han adaptado la extensión de palabras a segmentos con respecto al aprendizaje de transductores estocásticos de estados finitos mediante GIATI.…”