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1. Designing a Uniform Meaning Representation for Natural Language Processing

   https://doi.org/10.1007/s13218-021-00722-w  

   Van Gysel, Jens E.; Vigus, Meagan; Chun, Jayeol; Lai, Kenneth; Moeller, Sarah; Yao, Jiarui; O’Gorman, Tim; Cowell, Andrew; Croft, William; Huang, Chu-Ren; et al (April 2021, KI - Künstliche Intelligenz)

   null (Ed.)

   In this paper we present Uniform Meaning Representation (UMR), a meaning representation designed to annotate the semantic content of a text. UMR is primarily based on Abstract Meaning Representation (AMR), an annotation framework initially designed for English, but also draws from other meaning representations. UMR extends AMR to other languages, particularly morphologically complex, low-resource languages. UMR also adds features to AMR that are critical to semantic interpretation and enhances AMR by proposing a companion document-level representation that captures linguistic phenomena such as coreference as well as temporal and modal dependencies that potentially go beyond sentence boundaries. 

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2. Bootstrapping UMR Annotations for Arapaho from Language Documentation Resources

   Buchholz, Matthew J; Bonn, Julia; Post, Claire Benet; Cowell, Andrew; Palmer, Alexis (May 2024, ELRA and ICCL)

   Calzolari, Nicoletta; Kan, Min-Yen; Hoste, Veronique; Lenci, Alessandro; Sakti, Sakriani; Xue, Nianwen (Ed.)

   Uniform Meaning Representation (UMR) is a semantic labeling system in the AMR family designed to be uniformly applicable to typologically diverse languages. The UMR labeling system is quite thorough and can be time-consuming to execute, especially if annotators are starting from scratch. In this paper, we focus on methods for bootstrapping UMR annotations for a given language from existing resources, and specifically from typical products of language documentation work, such as lexical databases and interlinear glossed text (IGT). Using Arapaho as our test case, we present and evaluate a bootstrapping process that automatically generates UMR subgraphs from IGT. Additionally, we describe and evaluate a method for bootstrapping valency lexicon entries from lexical databases for both the target language and English. We are able to generate enough basic structure in UMR graphs from the existing Arapaho interlinearized texts to automate UMR labeling to a significant extent. Our method thus has the potential to streamline the process of building meaning representations for new languages without existing large-scale computational resources. 

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3. Demonstratives as attention tools: Evidence of mentalistic representations within language

   https://doi.org/10.1073/pnas.2402068121  

   Jara-Ettinger, Julian; Rubio-Fernandez, Paula (August 2024, Proceedings of the National Academy of Sciences)

   Linguistic communication is an intrinsically social activity that enables us to share thoughts across minds. Many complex social uses of language can be captured by domain-general representations of other minds (i.e., mentalistic representations) that externally modulate linguistic meaning through Gricean reasoning. However, here we show that representations of others’ attention are embedded within language itself. Across ten languages, we show that demonstratives—basic grammatical words (e.g., “this”/“that”) which are evolutionarily ancient, learned early in life, and documented in all known languages—are intrinsic attention tools. Beyond their spatial meanings, demonstratives encode both joint attention and the direction in which the listener must turn to establish it. Crucially, the frequency of the spatial and attentional uses of demonstratives varies across languages, suggesting that both spatial and mentalistic representations are part of their conventional meaning. Using computational modeling, we show that mentalistic representations of others’ attention are internally encoded in demonstratives, with their effect further boosted by Gricean reasoning. Yet, speakers are largely unaware of this, incorrectly reporting that they primarily capture spatial representations. Our findings show that representations of other people’s cognitive states (namely, their attention) are embedded in language and suggest that the most basic building blocks of the linguistic system crucially rely on social cognition. 

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4. Using MRS for Semantic Representation in Task-Oriented Dialogue

   George, Denson; Khalid, Baber; Stone, Matthew (August 2025, https://aclanthology.org/2025.dmr-1.4/)

   Lai, Kenneth; Wein, Shira (Ed.)

   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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5. Knowledge-driven Natural Language Understanding of English Text and its Applications

   Basu, Kinjal; Varanasi, Sarat C.; Shakerin, Farhad; Arias, Joaquin; Gupta, Gopal (May 2021, Proceedings of the AAAI Conference on Artificial Intelligence)

   null (Ed.)

   Understanding the meaning of a text is a fundamental challenge of natural language understanding (NLU) research. An ideal NLU system should process a language in a way that is not exclusive to a single task or a dataset. Keeping this in mind, we have introduced a novel knowledge driven semantic representation approach for English text. By leveraging the VerbNet lexicon, we are able to map syntax tree of the text to its commonsense meaning represented using basic knowledge primitives. The general purpose knowledge represented from our approach can be used to build any reasoning based NLU system that can also provide justification. We applied this approach to construct two NLU applications that we present here: SQuARE (Semantic-based Question Answering and Reasoning Engine) and StaCACK (Stateful Conversational Agent using Commonsense Knowledge). Both these systems work by ``truly understanding'' the natural language text they process and both provide natural language explanations for their responses while maintaining high accuracy. 

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