@inproceedings{post-etal-2026-adding,
title = "Adding Aspectual Information to Structured Meaning Representations",
author = "Post, Claire Benet and
Bontempo, Paul and
Milliken, August Ulfelder and
Chen, Alvin Po-Chun and
Derby, Nicholas and
Khatwani, Saksham and
Nabieva, Sumeyye and
Sairam, Karthik and
Palmer, Alexis",
editor = "Zhao, Jin and
Post, Claire Benet and
Hoefer, Elizabeth",
booktitle = "Proceedings of The Seventh International Workshop on Designing Meaning Representations ({DMR} 2026) @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.dmr-1.2/",
doi = "10.63317/33sxm7n8f7qd",
pages = "20--36",
abstract = "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."
}<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="post-etal-2026-adding">
<titleInfo>
<title>Adding Aspectual Information to Structured Meaning Representations</title>
</titleInfo>
<name type="personal">
<namePart type="given">Claire</namePart>
<namePart type="given">Benet</namePart>
<namePart type="family">Post</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Paul</namePart>
<namePart type="family">Bontempo</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">August</namePart>
<namePart type="given">Ulfelder</namePart>
<namePart type="family">Milliken</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alvin</namePart>
<namePart type="given">Po-Chun</namePart>
<namePart type="family">Chen</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Nicholas</namePart>
<namePart type="family">Derby</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Saksham</namePart>
<namePart type="family">Khatwani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Sumeyye</namePart>
<namePart type="family">Nabieva</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Karthik</namePart>
<namePart type="family">Sairam</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Alexis</namePart>
<namePart type="family">Palmer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2026-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of The Seventh International Workshop on Designing Meaning Representations (DMR 2026) @ LREC 2026</title>
</titleInfo>
<name type="personal">
<namePart type="given">Jin</namePart>
<namePart type="family">Zhao</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Claire</namePart>
<namePart type="given">Benet</namePart>
<namePart type="family">Post</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Elizabeth</namePart>
<namePart type="family">Hoefer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA Language Resources Association (ELRA)</publisher>
<place>
<placeTerm type="text">Palma, Mallorca (Spain)</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>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.</abstract>
<identifier type="citekey">post-etal-2026-adding</identifier>
<identifier type="doi">10.63317/33sxm7n8f7qd</identifier>
<location>
<url>https://aclanthology.org/2026.dmr-1.2/</url>
</location>
<part>
<date>2026-05</date>
<extent unit="page">
<start>20</start>
<end>36</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Adding Aspectual Information to Structured Meaning Representations
%A Post, Claire Benet
%A Bontempo, Paul
%A Milliken, August Ulfelder
%A Chen, Alvin Po-Chun
%A Derby, Nicholas
%A Khatwani, Saksham
%A Nabieva, Sumeyye
%A Sairam, Karthik
%A Palmer, Alexis
%Y Zhao, Jin
%Y Post, Claire Benet
%Y Hoefer, Elizabeth
%S Proceedings of The Seventh International Workshop on Designing Meaning Representations (DMR 2026) @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F post-etal-2026-adding
%X 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.
%R 10.63317/33sxm7n8f7qd
%U https://aclanthology.org/2026.dmr-1.2/
%U https://doi.org/10.63317/33sxm7n8f7qd
%P 20-36
Markdown (Informal)
[Adding Aspectual Information to Structured Meaning Representations](https://aclanthology.org/2026.dmr-1.2/) (Post et al., DMR 2026)
ACL
- Claire Benet Post, Paul Bontempo, August Ulfelder Milliken, Alvin Po-Chun Chen, Nicholas Derby, Saksham Khatwani, Sumeyye Nabieva, Karthik Sairam, and Alexis Palmer. 2026. Adding Aspectual Information to Structured Meaning Representations. In Proceedings of The Seventh International Workshop on Designing Meaning Representations (DMR 2026) @ LREC 2026, pages 20–36, Palma, Mallorca (Spain). ELRA Language Resources Association (ELRA).