Abstract
Some applications of artificial intelligence make it desirable that logical formulae be converted computationally to comprehensible natural language sentences. As there are many logical equivalents to a given formula, finding the most suitable equivalent to be used as input for such a ``logic-to-text'' generation system is a difficult challenge. In this paper, we focus on the role of brevity: Are the shortest formulae the most suitable? We focus on propositional logic (PL), framing formula minimization (i.e., the problem of finding the shortest equivalent of a given formula) as a Quantified Boolean Formulae (QBFs) satisfiability problem. We experiment with several generators and selection strategies to prune the resulting candidates. We conduct exhaustive automatic and human evaluations of the comprehensibility and fluency of the generated texts. The results suggest that while, in many cases, minimization has a positive impact on the quality of the sentences generated, formula minimization may ultimately not be the best strategy.
Original language | English |
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Title of host publication | StarSEM 2023 - 12th Joint Conference on Lexical and Computational Semantics, Proceedings of the Conference |
Editors | Alexis Palmer, Jose Camacho-collados |
Place of Publication | Toronto, Canada |
Publisher | Association for Computational Linguistics |
Pages | 180-192 |
Number of pages | 13 |
ISBN (Electronic) | 9781959429760 |
ISBN (Print) | 9781959429760 |
DOIs | |
Publication status | Published - 1 Jul 2023 |
Publication series
Name | Proceedings of the Annual Meeting of the Association for Computational Linguistics |
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ISSN (Print) | 0736-587X |
Bibliographical note
Publisher Copyright:© 2023 Association for Computational Linguistics.
Funding
This project has received funding from the European Union’s Horizon 2020 research and innovation pro gram under the Marie Skłodowska- Curie grant agreement № 860621.
Funders | Funder number |
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Horizon 2020 Framework Programme | 860621 |