In this paper, we present a submission to the Touché lab's Task 2 on Argument Retrieval for Comparative Questions [1, 2]. Our team Katana supplies several approaches based on decision tree ensembles algorithms to rank comparative documents in accordance with their relevance and argumentative support. We use PyTerrier  library to apply ensembles models to a ranking problem, considering statistical text features and features based on comparative structures. We also employ large contextualized language modelling techniques, such as BERT , to solve the proposed ranking task. To merge this technique with ranking modelling, we leverage neural ranking library OpenNIR . Our systems substantially outperforming the proposed baseline and scored first in relevance and second in quality according to the official metrics of the competition (for measure NDCG@5 score). Presented models could help to improve the performance of processing comparative queries in information retrieval and dialogue systems.
|Журнал||CEUR Workshop Proceedings|
|Состояние||Опубликовано - 2021|
|Событие||2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021 - Virtual, Bucharest, Румыния|
Продолжительность: 21 сент. 2021 → 24 сент. 2021