# X-AMR Annotation Tool

arXiv: https://arxiv.org/abs/2403.15407 · HTML: https://mlsanity.com/paper/2403.15407/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 23 references that are in the map.
- First posted on arXiv 29 Feb 2024.

## Builds on (most cited first, 8 of 8)

- [MAVEN: A Massive General Domain Event Detection Dataset](https://mlsanity.com/paper/2004.13590/) (2020)
- [Graph Pre-training for AMR Parsing and Generation](https://mlsanity.com/paper/2203.07836/) (2022)
- [Leveraging Abstract Meaning Representation for Knowledge Base Question Answering](https://mlsanity.com/paper/2012.01707/) (2020)
- [FactGraph: Evaluating Factuality in Summarization with Semantic Graph Representations](https://mlsanity.com/paper/2204.06508/) (2022)
- [Decomposing Complex Questions Makes Multi-Hop QA Easier and More Interpretable](https://mlsanity.com/paper/2110.13472/) (2021)
- [Semantic Representation for Dialogue Modeling](https://mlsanity.com/paper/2105.10188/) (2021)
- [2 * n is better than n2: Decomposing Event Coreference Resolution into Two Tractable Problems](https://mlsanity.com/paper/2305.05672/) (2023)
- [How Good is the Model in Model-in-the-loop Event Coreference Resolution Annotation?](https://mlsanity.com/paper/2306.05434/) (2023)

## 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