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        <title>Semantic Composition with PSHRG for Derivation Tree Reconstruction from Graph-Based Meaning Representations</title>
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    <abstract>We introduce a data-driven approach to generating derivation trees from meaning representation graphs with probabilistic synchronous hyperedge replacement grammar (PSHRG). SHRG has been used to produce meaning representation graphs from texts and syntax trees, but little is known about its viability on the reverse. In particular, we experiment on Dependency Minimal Recursion Semantics (DMRS) and adapt PSHRG as a formalism that approximates the semantic composition of DMRS graphs and simultaneously recovers the derivations that license the DMRS graphs. Consistent results are obtained as evaluated on a collection of annotated corpora. This work reveals the ability of PSHRG in formalizing a syntax–semantics interface, modelling compositional graph-to-tree translations, and channelling explainability to surface realization.</abstract>
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%0 Conference Proceedings
%T Semantic Composition with PSHRG for Derivation Tree Reconstruction from Graph-Based Meaning Representations
%A Lo, Chun Hei
%A Lam, Wai
%A Cheng, Hong
%Y Muresan, Smaranda
%Y Nakov, Preslav
%Y Villavicencio, Aline
%S Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F lo-etal-2022-semantic
%X We introduce a data-driven approach to generating derivation trees from meaning representation graphs with probabilistic synchronous hyperedge replacement grammar (PSHRG). SHRG has been used to produce meaning representation graphs from texts and syntax trees, but little is known about its viability on the reverse. In particular, we experiment on Dependency Minimal Recursion Semantics (DMRS) and adapt PSHRG as a formalism that approximates the semantic composition of DMRS graphs and simultaneously recovers the derivations that license the DMRS graphs. Consistent results are obtained as evaluated on a collection of annotated corpora. This work reveals the ability of PSHRG in formalizing a syntax–semantics interface, modelling compositional graph-to-tree translations, and channelling explainability to surface realization.
%R 10.18653/v1/2022.acl-long.372
%U https://aclanthology.org/2022.acl-long.372
%U https://doi.org/10.18653/v1/2022.acl-long.372
%P 5425-5439

Markdown (Informal)

[Semantic Composition with PSHRG for Derivation Tree Reconstruction from Graph-Based Meaning Representations](https://aclanthology.org/2022.acl-long.372) (Lo et al., ACL 2022)

• Semantic Composition with PSHRG for Derivation Tree Reconstruction from Graph-Based Meaning Representations (Lo et al., ACL 2022)

ACL

• Chun Hei Lo, Wai Lam, and Hong Cheng. 2022. Semantic Composition with PSHRG for Derivation Tree Reconstruction from Graph-Based Meaning Representations. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 5425–5439, Dublin, Ireland. Association for Computational Linguistics.