12 citations · 21 across the 6 of their papers we have counts for
8 papers
The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic
Thomas Elmar Kolb, Irina Nalis, Mete Sertkan +1
News recommender systems (NRs) have been shown to shape public discourse and to enforce behaviors that have a critical, oftentimes detrimental effect on democracies. Earlier resear…
Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction
Sebastian Hofstätter, Omar Khattab, Sophia Althammer +2
Recent progress in neural information retrieval has demonstrated large gains in effectiveness, while often sacrificing the efficiency and interpretability of the neural model compa…
Establishing Strong Baselines for TripClick Health Retrieval
Sebastian Hofstätter, Sophia Althammer, Mete Sertkan +1
We present strong Transformer-based re-ranking and dense retrieval baselines for the recently released TripClick health ad-hoc retrieval collection. We improve the - originally too…
A Time-Optimized Content Creation Workflow for Remote Teaching
Sebastian Hofstätter, Sophia Althammer, Mete Sertkan +1
We describe our workflow to create an engaging remote learning experience for a university course, while minimizing the post-production time of the educators. We make use of ubiqui…
Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation
Sebastian Hofstätter, Sophia Althammer, Michael Schröder +2
Retrieval and ranking models are the backbone of many applications such as web search, open domain QA, or text-based recommender systems. The latency of neural ranking models at qu…
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…