World Knowledge Resolves Some Aspectual Ambiguity

Annotating event descriptions with their aspectual features is often seen as a pre-requisite to temporal reasoning.However, a recent study by Pru et al. (2024) has shown that non-experts' annotations of the aspectual class of English verb phrases can disagree with both expert linguistic annotations and each another.They hypothesised that people use their world knowledge to tacitly conjure their own contexts, leading to disagreement between them.In this paper, we test that hypothesis by adding context to Pru et al.'s examples and mirroring their experiment.Our results show that whilst their hypothesis explains some of the disagreement, some examples continue to yield divided responses even with the additional context.Finally, we show that outputs from GPT-4, despite to some degree capturing the aspectual class division, are not an accurate predictor of human answers.

Authors

Institutions

Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.findings-acl.683
Primary Topic
Epistemology, Ethics, and Metaphysics
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

World Knowledge Resolves Some Aspectual Ambiguity

Mark Steedman, Adam Lopez, Katarzyna Pruś
Epistemology, Ethics, and Metaphysics
article

World Knowledge Resolves Some Aspectual Ambiguity

Mark Steedman, Adam Lopez, Katarzyna Pruś
article en

Abstract

Annotating event descriptions with their aspectual features is often seen as a pre-requisite to temporal reasoning.However, a recent study by Pru et al. (2024) has shown that non-experts' annotations of the aspectual class of English verb phrases can disagree with both expert linguistic annotations and each another.They hypothesised that people use their world knowledge to tacitly conjure their own contexts, leading to disagreement between them.In this paper, we test that hypothesis by adding context to Pru et al.'s examples and mirroring their experiment.Our results show that whilst their hypothesis explains some of the disagreement, some examples continue to yield divided responses even with the additional context.Finally, we show that outputs from GPT-4, despite to some degree capturing the aspectual class division, are not an accurate predictor of human answers.

University of Edinburgh (GB)
Engineering and Physical Sciences Research Council
Openalex Percentile: Top 81%
Epistemology, Ethics, and Metaphysics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.