Abstract
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.
Original language | English |
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Title of host publication | Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics |
Place of Publication | Minneapolis, Minnesota |
Publisher | Association for Computational Linguistics |
Pages | 902–911 |
Number of pages | 9 |
Volume | 1 |
Publication status | Published - 7 Jun 2019 |
Event | 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics - Minneapolis, United States Duration: 2 Jun 2019 → 7 Jun 2019 https://naacl2019.org/ |
Conference
Conference | 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics |
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Abbreviated title | NAACL-HLT 2019 |
Country/Territory | United States |
City | Minneapolis |
Period | 2/06/19 → 7/06/19 |
Internet address |