# Towards Self-Contained Answers: Entity-Based Answer Rewriting in Conversational Search

arXiv: https://arxiv.org/abs/2403.01747 · HTML: https://mlsanity.com/paper/2403.01747/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 41 references that are in the map.
- First posted on arXiv 4 Mar 2024.

## Builds on (most cited first, 19 of 19)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [Precise Zero-Shot Dense Retrieval without Relevance Labels](https://mlsanity.com/paper/2212.10496/) (2022)
- [QuAC : Question Answering in Context](https://mlsanity.com/paper/1808.07036/) (2018)
- [Open-Domain Question Answering Goes Conversational via Question Rewriting](https://mlsanity.com/paper/2010.04898/) (2020)
- [Asking Clarifying Questions in Open-Domain Information-Seeking Conversations](https://mlsanity.com/paper/1907.06554/) (2019)
- [Few-Shot Generative Conversational Query Rewriting](https://mlsanity.com/paper/2006.05009/) (2020)
- [ACUTE-EVAL: Improved Dialogue Evaluation with Optimized Questions and Multi-turn Comparisons](https://mlsanity.com/paper/1909.03087/) (2019)
- [Few-Shot Conversational Dense Retrieval](https://mlsanity.com/paper/2105.04166/) (2021)
- [TREC CAsT 2019: The Conversational Assistance Track Overview](https://mlsanity.com/paper/2003.13624/) (2020)
- [Visconde: Multi-document QA with GPT-3 and Neural Reranking](https://mlsanity.com/paper/2212.09656/) (2022)
- [A Comparison of Question Rewriting Methods for Conversational Passage Retrieval](https://mlsanity.com/paper/2101.07382/) (2021)
- [Conversational Entity Linking: Problem Definition and Datasets](https://mlsanity.com/paper/2105.04903/) (2021)
- [Personal Entity, Concept, and Named Entity Linking in Conversations](https://mlsanity.com/paper/2206.07836/) (2022)
- [SWAT: A System for Detecting Salient Wikipedia Entities in Texts](https://mlsanity.com/paper/1804.03580/) (2018)
- [Searching to Learn with Instructional Scaffolding](https://mlsanity.com/paper/2111.14584/) (2021)
- [Relevance Models Based on the Knowledge Gap](https://mlsanity.com/paper/2204.11568/) (2022)

## Built on by (most cited first, 3 of 3)

- [GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction](https://mlsanity.com/paper/2504.10792/) (2025)
- [Engineering Conversational Search Systems: A Review of Applications, Architectures, and Functional Components](https://mlsanity.com/paper/2407.00997/) (2024)
- [Conversational Exploratory Search of Scholarly Publications Using Knowledge Graphs](https://mlsanity.com/paper/2410.00427/) (2024)

## Abstract (arXiv)

Conversational information-seeking (CIS) is an emerging paradigm for knowledge acquisition and exploratory search. Traditional web search interfaces enable easy exploration of entities, but this is limited in conversational settings due to the limited-bandwidth interface. This paper explore ways to rewrite answers in CIS, so that users can understand them without having to resort to external services or sources. Specifically, we focus on salient entities -- entities that are central to understanding the answer. As our first contribution, we create a dataset of conversations annotated with entities for saliency. Our analysis of the collected data reveals that the majority of answers contain salient entities. As our second contribution, we propose two answer rewriting strategies aimed at improving the overall user experience in CIS. One approach expands answers with inline definitions of salient entities, making the answer self-contained. The other approach complements answers with follow-up questions, offering users the possibility to learn more about specific entities. Results of a crowdsourcing-based study indicate that rewritten answers are clearly preferred over the original ones. We also find that inline definitions tend to be favored over follow-up questions, but this choice is highly subjective, thereby providing a promising future direction for personalization.