# Improving Conversational Passage Re-ranking with View Ensemble

arXiv: https://arxiv.org/abs/2304.13290 · HTML: https://mlsanity.com/paper/2304.13290/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Apr 2023.

## Built on by (most cited first, 2 of 2)

- [A Survey of Conversational Search](https://mlsanity.com/paper/2410.15576/) (2024)
- [Ontology-Guided Query Expansion for Biomedical Document Retrieval using Large Language Models](https://mlsanity.com/paper/2508.11784/) (2025)

## Abstract (arXiv)

This paper presents ConvRerank, a conversational passage re-ranker that employs a newly developed pseudo-labeling approach. Our proposed view-ensemble method enhances the quality of pseudo-labeled data, thus improving the fine-tuning of ConvRerank. Our experimental evaluation on benchmark datasets shows that combining ConvRerank with a conversational dense retriever in a cascaded manner achieves a good balance between effectiveness and efficiency. Compared to baseline methods, our cascaded pipeline demonstrates lower latency and higher top-ranking effectiveness. Furthermore, the in-depth analysis confirms the potential of our approach to improving the effectiveness of conversational search.