On Coherence-based Predictors for Dense Query Performance Prediction

Understand

Query Performance Prediction (QPP) estimates the effectiveness of a search engine's results in response to a query without relevance judgments.

• Traditionally, post-retrieval predictors have focused upon either the distribution of the retrieval scores, or the coherence of the top-ranked documents using traditional bag-of-words index representations.
• More recently, BERT-based models using dense embedded document representations have been used to create new predictors, but mostly applied to predict the performance of rankings created by BM25.
• Instead, we aim to predict the effectiveness of rankings created by single-representation dense retrieval models (ANCE & TCT-ColBERT).