# Conversational Entity Linking: Problem Definition and Datasets

arXiv: https://arxiv.org/abs/2105.04903 · HTML: https://mlsanity.com/paper/2105.04903/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 64 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 28 of 28)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Personalizing Dialogue Agents: I have a dog, do you have pets too?](https://mlsanity.com/paper/1801.07243/) (2018)
- [Reading Wikipedia to Answer Open-Domain Questions](https://mlsanity.com/paper/1704.00051/) (2017)
- [MultiWOZ -- A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling](https://mlsanity.com/paper/1810.00278/) (2018)
- [CoQA: A Conversational Question Answering Challenge](https://mlsanity.com/paper/1808.07042/) (2018)
- [Wizard of Wikipedia: Knowledge-Powered Conversational agents](https://mlsanity.com/paper/1811.01241/) (2018)
- [ERNIE: Enhanced Language Representation with Informative Entities](https://mlsanity.com/paper/1905.07129/) (2019)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [Autoregressive Entity Retrieval](https://mlsanity.com/paper/2010.00904/) (2020)
- [Knowledge Enhanced Contextual Word Representations](https://mlsanity.com/paper/1909.04164/) (2019)
- [MultiWOZ 2.2 : A Dialogue Dataset with Additional Annotation Corrections and State Tracking Baselines](https://mlsanity.com/paper/2007.12720/) (2020)
- [Neural Approaches to Conversational AI](https://mlsanity.com/paper/1809.08267/) (2018)
- [Open-Domain Question Answering Goes Conversational via Question Rewriting](https://mlsanity.com/paper/2010.04898/) (2020)
- [Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset](https://mlsanity.com/paper/1909.05358/) (2019)
- [Key-Value Retrieval Networks for Task-Oriented Dialogue](https://mlsanity.com/paper/1705.05414/) (2017)
- [E-BERT: Efficient-Yet-Effective Entity Embeddings for BERT](https://mlsanity.com/paper/1911.03681/) (2019)
- [Efficient One-Pass End-to-End Entity Linking for Questions](https://mlsanity.com/paper/2010.02413/) (2020)
- [REL: An Entity Linker Standing on the Shoulders of Giants](https://mlsanity.com/paper/2006.01969/) (2020)
- [Neural Open Information Extraction](https://mlsanity.com/paper/1805.04270/) (2018)
- [DeepType: Multilingual Entity Linking by Neural Type System Evolution](https://mlsanity.com/paper/1802.01021/) (2018)
- [Introducing MANtIS: a novel Multi-Domain Information Seeking Dialogues Dataset](https://mlsanity.com/paper/1912.04639/) (2019)
- [Word-Entity Duet Representations for Document Ranking](https://mlsanity.com/paper/1706.06636/) (2017)
- [Listening between the Lines: Learning Personal Attributes from Conversations](https://mlsanity.com/paper/1904.10887/) (2019)
- [Measuring Semantic Coherence of a Conversation](https://mlsanity.com/paper/1806.06411/) (2018)
- [Graph-Embedding Empowered Entity Retrieval](https://mlsanity.com/paper/2005.02843/) (2020)
- [SlugNERDS: A Named Entity Recognition Tool for Open Domain Dialogue Systems](https://mlsanity.com/paper/1805.03784/) (2018)
- [Query Understanding via Entity Attribute Identification](https://mlsanity.com/paper/1809.08566/) (2018)

## Built on by (most cited first, 11 of 11)

- [Conversational Question Answering on Heterogeneous Sources](https://mlsanity.com/paper/2204.11677/) (2022)
- [Learning to Relate to Previous Turns in Conversational Search](https://mlsanity.com/paper/2306.02553/) (2023)
- [A Survey on Recent Advances in Conversational Data Generation](https://mlsanity.com/paper/2405.13003/) (2024)
- [GraphWOZ: Dialogue Management with Conversational Knowledge Graphs](https://mlsanity.com/paper/2211.12852/) (2022)
- [Linking Surface Facts to Large-Scale Knowledge Graphs](https://mlsanity.com/paper/2310.14909/) (2023)
- [Personal Entity, Concept, and Named Entity Linking in Conversations](https://mlsanity.com/paper/2206.07836/) (2022)
- [Towards Self-Contained Answers: Entity-Based Answer Rewriting in Conversational Search](https://mlsanity.com/paper/2403.01747/) (2024)
- [Real World Conversational Entity Linking Requires More Than Zeroshots](https://mlsanity.com/paper/2409.01152/) (2024)
- [RAGAPHENE: A RAG Annotation Platform with Human Enhancements and Edits](https://mlsanity.com/paper/2508.19272/) (2025)
- [A Longitudinal Study on Different Annotator Feedback Loops in Complex RAG Tasks](https://mlsanity.com/paper/2510.11897/) (2025)
- [You Know What I Mean: A Benchmark for Agentic Conversational Reference Grounding](https://mlsanity.com/paper/2608.29834/) (2026)

## Abstract (arXiv)

Machine understanding of user utterances in conversational systems is of utmost importance for enabling engaging and meaningful conversations with users. Entity Linking (EL) is one of the means of text understanding, with proven efficacy for various downstream tasks in information retrieval. In this paper, we study entity linking for conversational systems. To develop a better understanding of what EL in a conversational setting entails, we analyze a large number of dialogues from existing conversational datasets and annotate references to concepts, named entities, and personal entities using crowdsourcing. Based on the annotated dialogues, we identify the main characteristics of conversational entity linking. Further, we report on the performance of traditional EL systems on our Conversational Entity Linking dataset, ConEL, and present an extension to these methods to better fit the conversational setting. The resources released with this paper include annotated datasets, detailed descriptions of crowdsourcing setups, as well as the annotations produced by various EL systems. These new resources allow for an investigation of how the role of entities in conversations is different from that in documents or isolated short text utterances like queries and tweets, and complement existing conversational datasets.