TL;DR
Neural-IR-Explorer is a tool that allows users to analyze and understand the inner workings and results of neural re-ranking models in information retrieval, moving beyond traditional metric-based evaluation.
Contribution
It introduces a content-focused, interactive exploration tool for neural re-ranking results, enabling detailed inspection of semantic connections and model behavior.
Findings
Enhanced understanding of neural re-ranking results
Facilitates analysis of semantic connections in IR
Supports browsing and inspection of query-document pairs
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/
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