Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-Ranking Results
This addresses the need for interpretability in neural IR systems for researchers and practitioners, though it is incremental as it builds on existing visualization and analysis methods.
The paper tackles the problem of understanding neural re-ranking models in Information Retrieval by developing Neural-IR-Explorer, a tool that allows users to visually explore and inspect the inner workings and semantic connections in retrieval results, with the tool being publicly available online.
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/