KASYS at the NTCIR-18 SUSHI Task

Haruki Fujimaki, Makoto P. Kato

Abstract: This paper describes the KASYS team’s participation in the NTCIR-18 SUSHI Task by presenting a multi-level metadata aggregation and retrieval approach for Subtask A, which focuses on retrieving undigitized historical materials with sparse item-level metadata. Our system leverages the hierarchical organization of the data—comprising Box, Folder, and Item levels—by aggregating metadata from lower to higher levels and applying two search strategies (“Merge” and “Each”). We evaluate traditional BM25 alongside dense retrieval models (E5 and ColBERT) without fine-tuning, and hyperparameter optimization using Optuna is employed to determine the optimal weight for each level. Although our multi-level score aggregation strategy was designed to exploit the hierarchical structure of the data, it did not yield a significant performance improvement over a simpler BM25 baseline. Future work will explore improved preprocessing of noisy metadata, hybrid retrieval methods combining BM25 with dense re-ranking, and model fine-tuning to further enhance performance in searching undigitized archival collections.