# Bridging Anaphora Resolution as Question Answering

arXiv: https://arxiv.org/abs/2004.07898 · HTML: https://mlsanity.com/paper/2004.07898/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Apr 2020.

## Built on by (most cited first, 14 of 14)

- [Benchmarks for Automated Commonsense Reasoning: A Survey](https://mlsanity.com/paper/2302.04752/) (2023)
- [Holmes: A Benchmark to Assess the Linguistic Competence of Language Models](https://mlsanity.com/paper/2404.18923/) (2024)
- [Probing for Bridging Inference in Transformer Language Models](https://mlsanity.com/paper/2104.09400/) (2021)
- [Design Choices for Crowdsourcing Implicit Discourse Relations: Revealing the Biases Introduced by Task Design](https://mlsanity.com/paper/2304.00815/) (2023)
- [Text-based NP Enrichment](https://mlsanity.com/paper/2109.12085/) (2021)
- [Scoring Coreference Chains with Split-Antecedent Anaphors](https://mlsanity.com/paper/2205.12323/) (2022)
- [Stay Together: A System for Single and Split-antecedent Anaphora Resolution](https://mlsanity.com/paper/2104.05320/) (2021)
- [End-to-end Neural Information Status Classification](https://mlsanity.com/paper/2109.02753/) (2021)
- [Unifying the Scope of Bridging Anaphora Types in English: Bridging Annotations in ARRAU and GUM](https://mlsanity.com/paper/2410.01170/) (2024)
- [IRC-Bench: Recognizing Entities from Contextual Cues in First-Person Reminiscences](https://mlsanity.com/paper/2605.06142/) (2026)
- [Incorporating Singletons and Mention-based Features in Coreference Resolution via Multi-task Learning for Better Generalization](https://mlsanity.com/paper/2309.11582/) (2023)
- [Automatic Generation of Inference Making Questions for Reading Comprehension Assessments](https://mlsanity.com/paper/2506.08260/) (2025)
- [GUMBridge: a Corpus for Varieties of Bridging Anaphora](https://mlsanity.com/paper/2512.07134/) (2025)
- [LLMBridge: An LLM Pipeline for End-to-end Referential Bridging Resolution in English](https://mlsanity.com/paper/2605.29048/) (2026)

## Abstract (arXiv)

Most previous studies on bridging anaphora resolution (Poesio et al., 2004; Hou et al., 2013b; Hou, 2018a) use the pairwise model to tackle the problem and assume that the gold mention information is given. In this paper, we cast bridging anaphora resolution as question answering based on context. This allows us to find the antecedent for a given anaphor without knowing any gold mention information (except the anaphor itself). We present a question answering framework (BARQA) for this task, which leverages the power of transfer learning. Furthermore, we propose a novel method to generate a large amount of "quasi-bridging" training data. We show that our model pre-trained on this dataset and fine-tuned on a small amount of in-domain dataset achieves new state-of-the-art results for bridging anaphora resolution on two bridging corpora (ISNotes (Markert et al., 2012) and BASHI (Roesiger, 2018)).