To fully capture the meaning of a sentence, semantic representations should encode aspect, which describes the internal temporal structure of events. In graph-based meaning representation frameworks such as Uniform Meaning Representations (UMR), aspect lets one know how events unfold over time, including distinctions such as states, activities, and completed events. Despite its importance, aspect remains sparsely annotated across semantic meaning representation frameworks. This has, in turn, hindered not only current manual annotation, but also the development of automatic systems capable of predicting aspectual information. In this paper, we introduce a new dataset of English sentences annotated with UMR aspect labels over Abstract Meaning Representation (AMR) graphs that lack the feature. We describe the annotation scheme and guidelines used to label eventive predicates according to the UMR aspect lattice, as well as the annotation pipeline used to ensure consistency and quality across annotators through a multi-step adjudication process. To demonstrate the utility of our dataset for future automation, we perform simple baseline experiments using three modeling approaches. Our results establish initial benchmarks for automatic UMR aspect prediction and provide a foundation for integrating aspect into semantic meaning representations more broadly.

Current Abstract Meaning Representation (AMR) annotation guidelines, which largely tie argument structure to lexical rolesets, systematically misrepresent cases in which key semantic roles stem from clause-level structure rather than the verb, leaving these meanings either unnaturally attached, incorrect, or unexpressed. To address this limitation, we present CxGr-AMR, a novel extension of AMR that captures the semantics of various types of phrasal constructions, including argument structure constructions. We first examine how such cases are handled under current Standard-AMR guidelines and show that these analyses are often inadequate when constructionally contributed roles clash with those assigned by the verb. We then provide a theoretical grounding for our CxGr-AMR rolesets that lay out the relationship between the syntactic signatures of constructional slots and particular semantic roles associated with them. Finally, we develop an annotation-expert-in-the-loop pipeline for the semi-automatic annotation of sentences, and release a dataset containing 355 instances of phrasal constructions annotated with both Standard and CxGr-AMR.

This paper presents a multi-dialect text classifier for Quechua that augments neural models with rule-based linguistic information to address challenges in low-resource, morphologically complex settings. The approach is built on a carefully curated dataset spanning multiple genres, including annotated parallel bible corpora, and encodes manually annotated lexical variation and polypersonal verbal agreement as explicit features within a transformer-based classifier. Results show that neural models substantially outperform statistical baselines, enabling highly accurate multi-class classification across 27 Quechua dialects. The impact of linguistic augmentation is context-dependent: gains are minimal in high-resource settings but more pronounced in low-resource and cross-domain conditions. Overall, this work aims to contribute to the development of dialect-sensitive NLP methods for Quechua and other low-resource, morphologically rich languages.

This paper reports the first release of the UMR (Uniform Meaning Representation) data set. UMR is a graph-based meaning representation formalism consisting of a sentence-level graph and a document-level graph. The sentence-level graph represents predicate-argument structures, named entities, word senses, aspectuality of events, as well as person and number information for entities. The document-level graph represents coreferential, temporal, and modal relations that go beyond sentence boundaries. UMR is designed to capture the commonalities and variations across languages and this is done through the use of a common set of abstract concepts, relations, and attributes as well as concrete concepts derived from words from invidual languages. This UMR release includes annotations for six languages (Arapaho, Chinese, English, Kukama, Navajo, Sanapana) that vary greatly in terms of their linguistic properties and resource availability. We also describe on-going efforts to enlarge this data set and extend it to other genres and modalities. We also briefly describe the available infrastructure (UMR annotation guidelines and tools) that others can use to create similar data sets.

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.

Despite Uniform Meaning Representation’s (UMR) potential for cross-lingual semantics, limited annotated data has hindered its adoption. There are large datasets of English AMRs (Abstract Meaning Representations), but the process of converting AMR graphs to UMR graphs is non-trivial. In this paper we address a complex piece of that conversion process, namely cases where one AMR role can be mapped to multiple UMR roles through a non-deterministic process. We propose a neuro-symbolic method for role conversion, integrating animacy parsing and logic rules to guide a neural network, and minimizing human intervention. On test data, the model achieves promising accuracy, highlighting its potential to accelerate AMR-to-UMR conversion. Future work includes expanding animacy parsing, incorporating human feedback, and applying the method to broader aspects of conversion. This research demonstrates the benefits of combining symbolic and neural approaches for complex semantic tasks.

This paper describes the LECS Lab submission to the AmericasNLP 2024 Shared Task on the Creation of Educational Materials for Indigenous Languages. The task requires transforming a base sentence with regards to one or more linguistic properties (such as negation or tense). We observe that this task shares many similarities with the well-studied task of word-level morphological inflection, and we explore whether the findings from inflection research are applicable to this task. In particular, we experiment with a number of augmentation strategies, finding that they can significantly benefit performance, but that not all augmented data is necessarily beneficial. Furthermore, we find that our character-level neural models show high variability with regards to performance on unseen data, and may not be the best choice when training data is limited.