Retrieving comparative arguments using ensemble methods and neural information retrieval

Viktoriia Chekalina, Alexander Panchenko

Research output: Contribution to journalConference articlepeer-review

3 Citations (Scopus)

Abstract

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 [3] 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 [4], to solve the proposed ranking task. To merge this technique with ranking modelling, we leverage neural ranking library OpenNIR [5]. 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.

Original languageEnglish
Pages (from-to)2354-2365
Number of pages12
JournalCEUR Workshop Proceedings
Volume2936
Publication statusPublished - 2021
Event2021 Working Notes of CLEF - Conference and Labs of the Evaluation Forum, CLEF-WN 2021 - Virtual, Bucharest, Romania
Duration: 21 Sep 202124 Sep 2021

Keywords

  • Comparative argument retrieval
  • Natural language processing
  • Neural information retrieval

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