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This paper discusses the challenges of annotating the predicate-argument structure of Chinese verb compounds in Uniform Meaning Representation (UMR), a recent meaning representation framework that extends Abstract Meaning Representation (AMR) to cross-linguistic settings. The key issue is to decide whether to annotate the argument structure of a verb compound as a whole, or to annotate the argument structure of their component verbs as well as the relations between them. We examine different types of Chinese verb compounds, and propose how to annotate them based on the principle of compositionality, level of grammaticalization, and productivity of component verbs. We propose a solution to the practical problem of having to define the semantic roles for Chinese verb compounds that are quite open-ended by separating compositional verb compounds from verb compounds that are non-compositional or have grammaticalized verb components. For compositional verb compounds, instead of annotating the argument structure of the verb compound as a whole, we annotate the argument structure of the component verbs as well as the semantic relations between them as creating an exhaustive list of such verb compounds is infeasible. Verb compounds with grammaticalized verb components also tend to be productive and we represent grammaticalized verb compounds as either attributes of the primary verb or as relations.
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This content will become publicly available on August 4, 2026
Using MRS for Semantic Representation in Task-Oriented Dialogue
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.
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- PAR ID:
- 10639154
- Editor(s):
- Lai, Kenneth; Wein, Shira
- Publisher / Repository:
- https://aclanthology.org/2025.dmr-1.4/
- Date Published:
- Format(s):
- Medium: X
- Location:
- Prague, Cz
- Sponsoring Org:
- National Science Foundation
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