# CAMRA: Copilot for AMR Annotation

arXiv: https://arxiv.org/abs/2311.10928 · HTML: https://mlsanity.com/paper/2311.10928/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 18 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [CoRefi: A Crowd Sourcing Suite for Coreference Annotation](https://mlsanity.com/paper/2010.02588/) (2020)
- [How Good is the Model in Model-in-the-loop Event Coreference Resolution Annotation?](https://mlsanity.com/paper/2306.05434/) (2023)
- [AMR Normalization for Fairer Evaluation](https://mlsanity.com/paper/1909.01568/) (2019)
- [HUME: Human UCCA-Based Evaluation of Machine Translation](https://mlsanity.com/paper/1607.00030/) (2016)

## Abstract (arXiv)

In this paper, we introduce CAMRA (Copilot for AMR Annotatations), a cutting-edge web-based tool designed for constructing Abstract Meaning Representation (AMR) from natural language text. CAMRA offers a novel approach to deep lexical semantics annotation such as AMR, treating AMR annotation akin to coding in programming languages. Leveraging the familiarity of programming paradigms, CAMRA encompasses all essential features of existing AMR editors, including example lookup, while going a step further by integrating Propbank roleset lookup as an autocomplete feature within the tool. Notably, CAMRA incorporates AMR parser models as coding co-pilots, greatly enhancing the efficiency and accuracy of AMR annotators. To demonstrate the tool's capabilities, we provide a live demo accessible at: https://camra.colorado.edu