12 citations · 12 across the 3 of their papers we have counts for
6 papers
Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering
Sebastian Hofstätter, Markus Zlabinger, Mete Sertkan +2
There are many existing retrieval and question answering datasets. However, most of them either focus on ranked list evaluation or single-candidate question answering. This divide…
DEXA: Supporting Non-Expert Annotators with Dynamic Examples from Experts
Markus Zlabinger, Marta Sabou, Sebastian Hofstätter +2
The success of crowdsourcing based annotation of text corpora depends on ensuring that crowdworkers are sufficiently well-trained to perform the annotation task accurately. To that…
Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking
Sebastian Hofstätter, Markus Zlabinger, Allan Hanbury
Search engines operate under a strict time constraint as a fast response is paramount to user satisfaction. Thus, neural re-ranking models have a limited time-budget to re-rank doc…
DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations
Markus Zlabinger, Sebastian Hofstätter, Navid Rekabsaz +1
The effective extraction of ranked disease-symptom relationships is a critical component in various medical tasks, including computer-assisted medical diagnosis or the discovery of…
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…