Abstract
Traditional Information Retrieval (IR) systems mainly focus on answering questions about events or objects. However, there are various types of question forms that require IR systems to build complex answers from multiple data sources. Therefore, the idea of building IR systems that can create complex answers automatically, became the aim of TREC CAR 2017-2019. CAR (Complex Answer Retrieval) is one of many tracks, was hosted by TREC (The Text REtrieval Conference) where is a playground for the information retrieval community. In this paper, we built an improved complex answer retrieval system based on the system model of Nogueira et al. [3]. Our method tries to increase the coverage of the retrieval task. Thereby, the performance of our system shows that the MAP, MRR, and NDCG evaluation scores are improved.
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