X-AMR Annotation Tool
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Abstract (arXiv)
This paper presents a novel Cross-document Abstract Meaning Representation (X-AMR) annotation tool designed for annotating key corpus-level event semantics. Leveraging machine assistance through the Prodigy Annotation Tool, we enhance the user experience, ensuring ease and efficiency in the annotation process. Through empirical analyses, we demonstrate the effectiveness of our tool in augmenting an existing event corpus, highlighting its advantages when integrated with GPT-4. Code and annotations: https://github.com/ahmeshaf/gpt_coref
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 8 of its 23 references that are in the map. First posted on arXiv 29 Feb 2024.
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8 of 23 references.
References · 8
- 2020MAVEN: A Massive General Domain Event Detection Datasetcited by 103
- 2022Graph Pre-training for AMR Parsing and Generationcited by 55
- 2020Leveraging Abstract Meaning Representation for Knowledge Base Question Answeringcited by 53
- 2022FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representationscited by 41
- 2021Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretablecited by 35
- 2021Semantic Representation for Dialogue Modelingcited by 24
- 20232 * n is better than n2: Decomposing Event Coreference Resolution into Two Tractable Problemscited by 13
- 2023How Good is the Model in Model-in-the-loop Event Coreference Resolution Annotation?cited by 4
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