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In this paper, we present a submission to the Touche lab's Task 2 on ArgumentRetrieval for Comparative Questions. Our team Katana supplies severalapproaches based on decision tree ensembles algorithms to rank comparativedocuments in accordance with their relevance and argumentative support. We usePyTerrier library to apply ensembles models to a ranking problem, consideringstatistical text features and features based on comparative structures. We alsoemploy large contextualized language modelling techniques, such as BERT, tosolve the proposed ranking task. To merge this technique with rankingmodelling, we leverage neural ranking library OpenNIR.  Our systems substantially outperforming the proposed baseline and scoredfirst in relevance and second in quality according to the official metrics ofthe competition (for measure NDCG@5 score). Presented models could help toimprove the performance of processing comparative queries in informationretrieval and dialogue systems.

Retrieving Comparative Arguments using Ensemble Methods and Neural Information Retrieval