# Joint Coreference Resolution and Character Linking for Multiparty Conversation

arXiv: https://arxiv.org/abs/2101.11204 · HTML: https://mlsanity.com/paper/2101.11204/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 28 references that are in the map.
- First posted on arXiv 27 Jan 2021.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Higher-order Coreference Resolution with Coarse-to-fine Inference](https://mlsanity.com/paper/1804.05392/) (2018)
- [BERT for Coreference Resolution: Baselines and Analysis](https://mlsanity.com/paper/1908.09091/) (2019)
- [Coreference Resolution as Query-based Span Prediction](https://mlsanity.com/paper/1911.01746/) (2019)
- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [REL: An Entity Linker Standing on the Shoulders of Giants](https://mlsanity.com/paper/2006.01969/) (2020)
- [DeepType: Multilingual Entity Linking by Neural Type System Evolution](https://mlsanity.com/paper/1802.01021/) (2018)
- [Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models](https://mlsanity.com/paper/2008.05190/) (2020)
- [What You See is What You Get: Visual Pronoun Coreference Resolution in Dialogues](https://mlsanity.com/paper/1909.00421/) (2019)
- [Knowledge-aware Pronoun Coreference Resolution](https://mlsanity.com/paper/1907.03663/) (2019)
- [Incorporating Context and External Knowledge for Pronoun Coreference Resolution](https://mlsanity.com/paper/1905.10238/) (2019)
- [A Brief Survey and Comparative Study of Recent Development of Pronoun Coreference Resolution](https://mlsanity.com/paper/2009.12721/) (2020)

## Built on by (most cited first, 6 of 6)

- [Multi-level Contrastive Learning for Script-based Character Understanding](https://mlsanity.com/paper/2310.13231/) (2023)
- [Taxonomy of Abstractive Dialogue Summarization: Scenarios, Approaches and Future Directions](https://mlsanity.com/paper/2210.09894/) (2022)
- [Exophoric Pronoun Resolution in Dialogues with Topic Regularization](https://mlsanity.com/paper/2109.04787/) (2021)
- [Review of coreference resolution in English and Persian](https://mlsanity.com/paper/2211.04428/) (2022)
- [Persian Pronoun Resolution: Leveraging Neural Networks and Language Models](https://mlsanity.com/paper/2405.10714/) (2024)
- [Mah=an=ama: A Unique Testbed for Literary Entity Discovery and Linking](https://mlsanity.com/paper/2509.19844/) (2025)

## Abstract (arXiv)

Character linking, the task of linking mentioned people in conversations to the real world, is crucial for understanding the conversations. For the efficiency of communication, humans often choose to use pronouns (e.g., "she") or normal phrases (e.g., "that girl") rather than named entities (e.g., "Rachel") in the spoken language, which makes linking those mentions to real people a much more challenging than a regular entity linking task. To address this challenge, we propose to incorporate the richer context from the coreference relations among different mentions to help the linking. On the other hand, considering that finding coreference clusters itself is not a trivial task and could benefit from the global character information, we propose to jointly solve these two tasks. Specifically, we propose C2, the joint learning model of Coreference resolution and Character linking. The experimental results demonstrate that C2 can significantly outperform previous works on both tasks. Further analyses are conducted to analyze the contribution of all modules in the proposed model and the effect of all hyper-parameters.