The problem with probabilistic DAG automata for semantic graphs
This is an incremental result that identifies a limitation in probabilistic modeling for semantic graph representations, relevant to researchers in computational linguistics and graph theory.
The paper tackles the problem of creating probabilistic models for semantic DAGs by showing that DAG automata cannot be made into useful probabilistic models using common weight-assignment strategies, affecting most variants except planar ones, which have other issues.
Semantic representations in the form of directed acyclic graphs (DAGs) have been introduced in recent years, and to model them, we need probabilistic models of DAGs. One model that has attracted some attention is the DAG automaton, but it has not been studied as a probabilistic model. We show that some DAG automata cannot be made into useful probabilistic models by the nearly universal strategy of assigning weights to transitions. The problem affects single-rooted, multi-rooted, and unbounded-degree variants of DAG automata, and appears to be pervasive. It does not affect planar variants, but these are problematic for other reasons.