UQ IElab at TREC 2020 CAsT Track
2020 | TREC 2020 Decision Track
Authors:
Abstract
This paper describes the work done by the IELAB for the TREC Conversational Assistance Track (CAsT) 2020. IELAB investigated two methods to improve both the retrieval and re-ranking stages of a conversational IR system. The first method used an Adapted Query (AQ), which extracted context from the first utterance only to expand all subsequent queries for a conversational session. The second method utilized a query likelihood model (QLM) which used a pre-trained deep language model to estimate the likelihood that a query could be generated by a document. Specifically, we used the text-to-text transfer transformer (T5) as a scoring functions for re-ranking passages.