Linear Transduction Grammars and Zipper Finite-State Transducers

We examine how the recently explored class of linear transductions relates to finite-state models. Linear transductions have been neglected historically, but gainined recent interest in statistical machine translation modeling, due to empirical studies demonstrating that their attractive balance of generative capacity and complexity characteristics lead to improved accuracy and speed in learning alignment and translation models. Such work has until now characterized the class of linear transductions in terms of either (a) linear inversion transduction grammars (LITGs) which are linearized restrictions of inversion transduction grammars or (b) linear transduction grammars (LTGs) which are bilingualized generalizations of linear grammars. In this paper, we offer a new alternative characterization of linear transductions, as relating four finite-state languages to each other. We introduce the devices of zipper finite-state automata (ZFSAs) and zipper finite-state transducers (ZFSTs) in order to construct the bridge between linear transductions and finite-state models.