  This paper describes the participation of UvA.ILPS group at the TREC CAsT
2020 track. Our passage retrieval pipeline consists of (i) an initial retrieval
module that uses BM25, and (ii) a re-ranking module that combines the score of
a BERT ranking model with the score of a machine comprehension model adjusted
for passage retrieval. An important challenge in conversational passage
retrieval is that queries are often under-specified. Thus, we perform query
resolution, that is, add missing context from the conversation history to the
current turn query using QuReTeC, a term classification query resolution model.
We show that our best automatic and manual runs outperform the corresponding
median runs by a large margin.

Information Retrieval;Computation and Language