25 citations · 88 across the 23 of their papers we have counts for
4 papers · 2 filters
Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-Ranking Results
Sebastian Hofstätter, Markus Zlabinger, Allan Hanbury
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-Expl…
TU Wien @ TREC Deep Learning '19 -- Simple Contextualization for Re-ranking
Sebastian Hofstätter, Markus Zlabinger, Allan Hanbury
The usage of neural network models puts multiple objectives in conflict with each other: Ideally we would like to create a neural model that is effective, efficient, and interpreta…
Let's measure run time! Extending the IR replicability infrastructure to include performance aspects
Sebastian Hofstätter, Allan Hanbury
Establishing a docker-based replicability infrastructure offers the community a great opportunity: measuring the run time of information retrieval systems. The time required to pre…
On the Effect of Low-Frequency Terms on Neural-IR Models
Sebastian Hofstätter, Navid Rekabsaz, Carsten Eickhoff +1
Low-frequency terms are a recurring challenge for information retrieval models, especially neural IR frameworks struggle with adequately capturing infrequently observed words. Whil…