Published March 31, 2023 | Version 1
Conference paper Open

M4D-MKLab/ITI-CERTH Participation in TREC Deep Learning Track 2022

  • 1. Centre for Research & Technology Hellas, CERTH


Our team’s (CERTH ITI M4D) participation in TREC Deep Learning Track this year focused on the
use of the pretrained BERT model in Query Expansion. We submitted two runs in the document
retrieval subtask; both include a final reranking step. The first run incorporates a novel pooling
approach for the Contextualized Embedding Query Expansion (CEQE) methodology. The second run
introduces a novel term selection mechanism that complements the RM3 query expansion method by
filtering disadvantageous expansion terms. The term selection mechanism capitalizes on the BERT
model by fine tuning it to predict the quality of terms as expansion terms and can be used on any
query expansion technique.



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