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        <title>Using MRS for Semantic Representation in Task-Oriented Dialogue</title>
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    <abstract>Task-oriented dialogue (TOD) requires capabilities such as lookahead planning, reasoning, and belief state tracking, which continue to present challenges for end-to-end methods based on large language models (LLMs). As a possible method of addressing these concerns, we are exploring the integration of structured semantic representations with planning inferences. As a first step in this project, we describe an algorithm for generating Minimal Recursion Semantics (MRS) from dependency parses, obtained from a machine learning (ML) syntactic parser, and validate its performance on a challenging cooking domain. Specifically, we compare predicate-argument relations recovered by our approach with predicate-argument relations annotated using Abstract Meaning Representation (AMR). Our system is consistent with the gold standard in 94.1% of relations.</abstract>
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%0 Conference Proceedings
%T Using MRS for Semantic Representation in Task-Oriented Dialogue
%A George, Denson
%A Khalid, Baber
%A Stone, Matthew
%Y Lai, Kenneth
%Y Wein, Shira
%S Proceedings of the Sixth International Workshop on Designing Meaning Representations
%D 2025
%8 August
%I Association for Computational Lingustics
%C Prague, Czechia
%F george-etal-2025-using
%X Task-oriented dialogue (TOD) requires capabilities such as lookahead planning, reasoning, and belief state tracking, which continue to present challenges for end-to-end methods based on large language models (LLMs). As a possible method of addressing these concerns, we are exploring the integration of structured semantic representations with planning inferences. As a first step in this project, we describe an algorithm for generating Minimal Recursion Semantics (MRS) from dependency parses, obtained from a machine learning (ML) syntactic parser, and validate its performance on a challenging cooking domain. Specifically, we compare predicate-argument relations recovered by our approach with predicate-argument relations annotated using Abstract Meaning Representation (AMR). Our system is consistent with the gold standard in 94.1% of relations.
%U https://aclanthology.org/2025.dmr-1.4/
%P 30-37

Markdown (Informal)

[Using MRS for Semantic Representation in Task-Oriented Dialogue](https://aclanthology.org/2025.dmr-1.4/) (George et al., DMR 2025)

• Using MRS for Semantic Representation in Task-Oriented Dialogue (George et al., DMR 2025)

ACL

• Denson George, Baber Khalid, and Matthew Stone. 2025. Using MRS for Semantic Representation in Task-Oriented Dialogue. In Proceedings of the Sixth International Workshop on Designing Meaning Representations, pages 30–37, Prague, Czechia. Association for Computational Lingustics.