# CHAMP: Efficient Annotation and Consolidation of Cluster Hierarchies

arXiv: https://arxiv.org/abs/2311.11301 · HTML: https://mlsanity.com/paper/2311.11301/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 38 references that are in the map.
- First posted on arXiv 19 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [MAVEN-ERE: A Unified Large-scale Dataset for Event Coreference, Temporal, Causal, and Subevent Relation Extraction](https://mlsanity.com/paper/2211.07342/) (2022)
- [From Arguments to Key Points: Towards Automatic Argument Summarization](https://mlsanity.com/paper/2005.01619/) (2020)
- [Controlled Crowdsourcing for High-Quality QA-SRL Annotation](https://mlsanity.com/paper/1911.03243/) (2019)
- [QADiscourse -- Discourse Relations as QA Pairs: Representation, Crowdsourcing and Baselines](https://mlsanity.com/paper/2010.02815/) (2020)
- [SciCo: Hierarchical Cross-Document Coreference for Scientific Concepts](https://mlsanity.com/paper/2104.08809/) (2021)
- [YEDDA: A Lightweight Collaborative Text Span Annotation Tool](https://mlsanity.com/paper/1711.03759/) (2017)
- [CoRefi: A Crowd Sourcing Suite for Coreference Annotation](https://mlsanity.com/paper/2010.02588/) (2020)
- [From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization](https://mlsanity.com/paper/2306.03853/) (2023)
- [Model-based annotation of coreference](https://mlsanity.com/paper/1906.10724/) (2019)
- [ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution](https://mlsanity.com/paper/2210.07188/) (2022)

## Abstract (arXiv)

Various NLP tasks require a complex hierarchical structure over nodes, where each node is a cluster of items. Examples include generating entailment graphs, hierarchical cross-document coreference resolution, annotating event and subevent relations, etc. To enable efficient annotation of such hierarchical structures, we release CHAMP, an open source tool allowing to incrementally construct both clusters and hierarchy simultaneously over any type of texts. This incremental approach significantly reduces annotation time compared to the common pairwise annotation approach and also guarantees maintaining transitivity at the cluster and hierarchy levels. Furthermore, CHAMP includes a consolidation mode, where an adjudicator can easily compare multiple cluster hierarchy annotations and resolve disagreements.