bigIR at TREC 2021: Adopting Transfer Learning for News Background Linking

Abstract

In this paper, we present the participation of the bigIR team at Qatar University in the TREC 2021 news track. We participated in the background linking task. The task mainly aims to retrieve news articles that provide context and background knowledge to the reader of a specific query article. We submitted five runs for this task. In the first two, we adopted an ad-hoc retrieval approach, where the query articles were analyzed to generate search queries that were issued against the news articles collection to retrieve the required links. In the remaining runs, we adopted a transfer learning approach to rerank the articles retrieved given their usefulness to address specific subtopics related to the query articles. These subtopics were given by the track organizers as a new challenge this year. The results show that one of our runs outperformed TREC median submission, while others achieved comparable results.