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Establishing a good information retrieval system in popular mediums ofentertainment is a quickly growing area of investigation for companies andresearchers alike. We delve into the domain of information retrieval forpodcasts. In Spotify's Podcast Challenge, we are given a user's query with adescription to find the most relevant short segment from the given datasethaving all the podcasts. Previous techniques that include solely classicalInformation Retrieval (IR) techniques, perform poorly when descriptive queriesare presented. On the other hand, models which exclusively rely on large neuralnetworks tend to perform better. The downside to this technique is that aconsiderable amount of time and computing power are required to infer theresult. We experiment with two hybrid models which first filter out the bestpodcasts based on user's query with a classical IR technique, and then performre-ranking on the shortlisted documents based on the detailed description usinga transformer-based model.

LRG at TREC 2020: Document Ranking with XLNet-Based Models