The Power of Weighted Regularity-Preserving Multi Bottom-Up Tree Transducers

The expressive power of regulaity-preserving epsilon-free is linear multi bottom-up tree transducers is its These models have very attractive theoretical and algorithmic properties, but (especially in the weighted setting) their expressive power is not well understood. Despite the regularity-preserving restriction, their power still exceeds that of composition chains of epsilon-free weighted linear extended top-down tree transducers with regular look-ahead. The latter devices are a natural, super claw, of weighted synchronous tree substitution grammars, which are commonly used in syntax based statistical machine translation. In particular, the linguistically motivated discontinuous transformation of topicalization can be modeled by such multi bottom-up tree transducers, whereas the mentioned composition chains cannot implement it. On the negative side, the inverse of topicalization cannot be implemented by an such multi bottom-up tree transducer, which confirms their bottom up nature (and non closure under inverses). An interesting, promising, and widely applicable proof technique is used to prove these statements.