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.
@article{arxiv.2304.13290,
title = {Improving Conversational Passage Re-ranking with View Ensemble},
author = {Jia-Huei Ju and Sheng-Chieh Lin and Ming-Feng Tsai and Chuan-Ju Wang},
journal= {arXiv preprint arXiv:2304.13290},
year = {2023}
}