  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.
