Faster Learned Sparse Retrieval with Guided Traversal
Antonio Mallia, Joel Mackenzie, Torsten Suel, Nicola Tonellotto · Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval · 2022
Neural information retrieval architectures based on transformers such as BERT are able to significantly improve system effectiveness over traditional sparse models such as BM25. Though highly effective, these neural approaches are very expensive to run, making them difficult to deploy under strict latency constraints. To address this limitation, recent studies have proposed new families of learned sparse models that try to match the effectiveness of learned dense models, while leveraging the traditional inverted index data structure for efficiency.