# Real World Conversational Entity Linking Requires More Than Zeroshots

arXiv: https://arxiv.org/abs/2409.01152 · HTML: https://mlsanity.com/paper/2409.01152/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 2 Sep 2024.

## Builds on (most cited first, 14 of 14)

- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [The Pushshift Reddit Dataset](https://mlsanity.com/paper/2001.08435/) (2020)
- [Zero-Shot Entity Linking by Reading Entity Descriptions](https://mlsanity.com/paper/1906.07348/) (2019)
- [End-to-End Neural Entity Linking](https://mlsanity.com/paper/1808.07699/) (2018)
- [Efficient One-Pass End-to-End Entity Linking for Questions](https://mlsanity.com/paper/2010.02413/) (2020)
- [ConvoKit: A Toolkit for the Analysis of Conversations](https://mlsanity.com/paper/2005.04246/) (2020)
- [REL: An Entity Linker Standing on the Shoulders of Giants](https://mlsanity.com/paper/2006.01969/) (2020)
- [A Repository of Conversational Datasets](https://mlsanity.com/paper/1904.06472/) (2019)
- [Conversational Entity Linking: Problem Definition and Datasets](https://mlsanity.com/paper/2105.04903/) (2021)
- [Entity-aware Transformers for Entity Search](https://mlsanity.com/paper/2205.00820/) (2022)
- [ConCET: Entity-Aware Topic Classification for Open-Domain Conversational Agents](https://mlsanity.com/paper/2005.13798/) (2020)
- [Personal Entity, Concept, and Named Entity Linking in Conversations](https://mlsanity.com/paper/2206.07836/) (2022)
- [A Dataset for Sentence Retrieval for Open-Ended Dialogues](https://mlsanity.com/paper/2205.11685/) (2022)
- [Find the Funding: Entity Linking with Incomplete Funding Knowledge Bases](https://mlsanity.com/paper/2209.00351/) (2022)

## Built on by (most cited first, 1 of 1)

- [You Know What I Mean: A Benchmark for Agentic Conversational Reference Grounding](https://mlsanity.com/paper/2608.29834/) (2026)

## Abstract (arXiv)

Entity linking (EL) in conversations faces notable challenges in practical applications, primarily due to the scarcity of entity-annotated conversational datasets and sparse knowledge bases (KB) containing domain-specific, long-tail entities. We designed targeted evaluation scenarios to measure the efficacy of EL models under resource constraints. Our evaluation employs two KBs: Fandom, exemplifying real-world EL complexities, and the widely used Wikipedia. First, we assess EL models' ability to generalize to a new unfamiliar KB using Fandom and a novel zero-shot conversational entity linking dataset that we curated based on Reddit discussions on Fandom entities. We then evaluate the adaptability of EL models to conversational settings without prior training. Our results indicate that current zero-shot EL models falter when introduced to new, domain-specific KBs without prior training, significantly dropping in performance. Our findings reveal that previous evaluation approaches fall short of capturing real-world complexities for zero-shot EL, highlighting the necessity for new approaches to design and assess conversational EL models to adapt to limited resources. The evaluation setup and the dataset proposed in this research are made publicly available.