Year: 2026
Type: conference-paper
Abstract: Meaning Representation (AMR) is a graph-based semantic representation which captures the core elements of meaning of a text.AMR has been incorporated into a variety of downstream tasks, which rely heavily on the availability of gold-annotated AMR corpora.While the annotation process is fairly lightweight, annotator training is still required even for linguists due to the extensive nature of the annotation guidelines and comprehensive set of roles.Therefore, all corpus development projects for AMR (and extensions of AMR) require the dataset curators to first train annotators.In this paper, we develop an online AMR annotation training system called TrAinMR in order to ease this training process and thus motivate the development of additional AMR corpora.The two main components of TrAinMR are (1) a written tutorial covering the basics of AMR annotation, and (2) an interactive practice module with corrective feedback.To measure the effectiveness of this tool, we conduct two pilot studies with five human annotators each.We find that the majority of annotators state their understanding of AMR improved as a result of TrAinMR, and some annotators show a positive trend in SMATCH scores after completing the practice module.
Authors: Mina Yang, Shira Wein
Institution: Amherst College
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: hybrid
Funder: National Science Foundation
OpenAlex ID: W7166544348
FWCI:
Cites:
Cited by:
Related to: