Leveraging Query Resolution and Reading Comprehension for Conversational Passage Retrieval

Understand

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.