Abstract
In this paper we look beyond metrics-based evaluation of Information Retrieval systems, to explore the reasons behind ranking results. We present the content-focused Neural-IR-Explorer, which empowers users to browse through retrieval results and inspect the inner workings and fine-grained results of neural re-ranking models. The explorer includes a categorized overview of the available queries, as well as an individual query result view with various options to highlight semantic connections between query-document pairs. The Neural-IR-Explorer is available at: https://neural-ir-explorer.ec.tuwien.ac.at/.
Cite
CITATION STYLE
Hofstätter, S., Zlabinger, M., & Hanbury, A. (2020). Neural-IR-explorer: A content-focused tool to explore neural re-ranking results. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12036 LNCS, pp. 459–464). Springer. https://doi.org/10.1007/978-3-030-45442-5_58
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