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Semantic representations in the form of directed acyclic graphs (DAGs) havebeen introduced in recent years, and to model them, we need probabilisticmodels of DAGs. One model that has attracted some attention is the DAGautomaton, but it has not been studied as a probabilistic model. We show thatsome DAG automata cannot be made into useful probabilistic models by the nearlyuniversal strategy of assigning weights to transitions. The problem affectssingle-rooted, multi-rooted, and unbounded-degree variants of DAG automata, andappears to be pervasive. It does not affect planar variants, but these areproblematic for other reasons.

The problem with probabilistic DAG automata for semantic graphs