Normal Form on Linear Tree-to-Word Transducers

Abstract. We study a subclass of tree-to-word transducers: linear treeto-word transducers, that cannot use several copies of the input. We aim to study the equivalence problem on this class, by using minimization and normalization techniques. We identify a Myhill-Nerode characterization. It provides a minimal normal form on our class, computable in Exptime. This paper extends an already existing result on tree-to-word transducers without copy or reordering (sequential tree-to-word transducers), by accounting for all the possible reorderings in the output.

• Type: Journal
• Year: 2016
• Where: LATA
• Authors: Adrien Boiret
• Keywords: Artificial Intelligence, LATA 2016