Benchmark for Complex Answer Retrieval
This work addresses the challenge of complex answer retrieval for Wikipedia content creation, but it is incremental as it primarily benchmarks existing methods on a new dataset.
The paper tackles the problem of retrieving paragraphs for Wikipedia articles by introducing the TREC Complex Answer Retrieval (TREC CAR) dataset and presents early results from various retrieval methods, including tf-idf and deep neural networks, to guide future participants.
Retrieving paragraphs to populate a Wikipedia article is a challenging task. The new TREC Complex Answer Retrieval (TREC CAR) track introduces a comprehensive dataset that targets this retrieval scenario. We present early results from a variety of approaches -- from standard information retrieval methods (e.g., tf-idf) to complex systems that using query expansion using knowledge bases and deep neural networks. The goal is to offer future participants of this track an overview of some promising approaches to tackle this problem.