--- title: "Who cites PairSpanBERT: An Enhanced — 4 citing papers" description: "4 papers of the Elamur Research library cite «PairSpanBERT: An Enhanced Language Model for Bridging Resolution», found in their…" canonical: "https://research.elamur.ai/free/en/papers/kobayashi-2023-pairspanbert-enhanced-language-model-bridging/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kobayashi-2023-pairspanbert-enhanced-language-model-bridging/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [PairSpanBERT: An Enhanced Language Model for…](https://md.research.elamur.ai/free/en/papers/kobayashi-2023-pairspanbert-enhanced-language-model-bridging/) / cited by # Who cites «PairSpanBERT: An Enhanced Language Model for Bridging Resolution» 4 papers of our library cite it (of the 149,439 whose bibliographies we read) · 3 with a page here, the most cited first · [← back to «PairSpanBERT: An Enhanced Language Model for Bridging Resolution»](https://md.research.elamur.ai/free/en/papers/kobayashi-2023-pairspanbert-enhanced-language-model-bridging/) 1. [**Subjectivity in the Annotation of Bridging Anaphora**Lauren Levine, Amir Zeldes*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/levine-2025-subjectivity-annotation-bridging-anaphora/) 2. [**GUMBridge: A Corpus for Varieties of Bridging Anaphora**Lauren Levine, Amir Zeldes*2026 · 3 citations*](https://md.research.elamur.ai/free/en/papers/levine-2026-gumbridge-corpus-varieties-bridging-anaphora/) 3. [**LLMBridge: An LLM Pipeline for End-to-end Referential Bridging Resolution in English arXiv:2605.29048**Lauren Levine, Amir Zeldes*2026*](https://md.research.elamur.ai/free/en/papers/levine-2026-llmbridge-llm-pipeline-end-end/) 1 more paper of our library cites it; it has no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/kobayashi-2023-pairspanbert-enhanced-language-model-bridging/) — PairSpanBERT: An Enhanced Language Model for Bridging Resolution