Performance Prediction for Conversational Search Using Perplexities of Query Rewrites

We consider query performance prediction (QPP) task for conversational search (CS), i.e., to estimate the retrieval quality for queries in multi-turn conversations. We reuse QPP methods from ad-hoc search for CS by feeding them self-contained query rewrites generated by T5. Our experiments on three CS datasets show that (i) lower query rewriting quality may lead to worse QPP performance, and (ii) incorporating query rewriting quality (as measured by perplexity) improves the effectiveness of QPP methods for CS if the query rewriting quality is limited. Our implementation is publicly available at https://github.com/ChuanMeng/QPP4CS.