# Active Learning for Coreference Resolution using Discrete Annotation

arXiv: https://arxiv.org/abs/2004.13671 · HTML: https://mlsanity.com/paper/2004.13671/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Apr 2020.

## Built on by (most cited first, 12 of 12)

- [Efficient Methods for Natural Language Processing: A Survey](https://mlsanity.com/paper/2209.00099/) (2022)
- [A Survey of Active Learning for Natural Language Processing](https://mlsanity.com/paper/2210.10109/) (2022)
- [Interactive Natural Language Processing](https://mlsanity.com/paper/2305.13246/) (2023)
- [Multi-task Active Learning for Pre-trained Transformer-based Models](https://mlsanity.com/paper/2208.05379/) (2022)
- [Adapting Coreference Resolution Models through Active Learning](https://mlsanity.com/paper/2104.07611/) (2021)
- [CD2CR: Co-reference Resolution Across Documents and Domains](https://mlsanity.com/paper/2101.12637/) (2021)
- [Partially Supervised Named Entity Recognition via the Expected Entity Ratio Loss](https://mlsanity.com/paper/2108.07216/) (2021)
- [Improving Question Answering Performance Using Knowledge Distillation and Active Learning](https://mlsanity.com/paper/2109.12662/) (2021)
- [Aggregating Crowdsourced and Automatic Judgments to Scale Up a Corpus of Anaphoric Reference for Fiction and Wikipedia Texts](https://mlsanity.com/paper/2210.05581/) (2022)
- [ezCoref: Towards Unifying Annotation Guidelines for Coreference Resolution](https://mlsanity.com/paper/2210.07188/) (2022)
- [Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training](https://mlsanity.com/paper/2305.12634/) (2023)
- [Perturbation-based Active Learning for Question Answering](https://mlsanity.com/paper/2311.02345/) (2023)

## Abstract (arXiv)

We improve upon pairwise annotation for active learning in coreference resolution, by asking annotators to identify mention antecedents if a presented mention pair is deemed not coreferent. This simple modification, when combined with a novel mention clustering algorithm for selecting which examples to label, is much more efficient in terms of the performance obtained per annotation budget. In experiments with existing benchmark coreference datasets, we show that the signal from this additional question leads to significant performance gains per human-annotation hour. Future work can use our annotation protocol to effectively develop coreference models for new domains. Our code is publicly available at https://github.com/belindal/discrete-active-learning-coref .