Search Results for author:
Found 9 papers, 7 papers with code
Ranger: A Toolkit for Effect-Size Based Multi-Task Evaluation
In this paper, we introduce Ranger - a toolkit to facilitate the easy use of effect-size-based meta-analysis for multi-task evaluation in NLP and IR.
The Role of Bias in News Recommendation in the Perception of the Covid-19 Pandemic
Responsible NRs are supposed to have depolarizing capacities, once they go beyond accuracy measures.
Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction
Recent progress in neural information retrieval has demonstrated large gains in effectiveness, while often sacrificing the efficiency and interpretability of the neural model compared to classical approaches.
PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval
However in the web domain we are in a setting with large amounts of training data and a query-to-passage or a query-to-document retrieval task.
Establishing Strong Baselines for TripClick Health Retrieval
We present strong Transformer-based re-ranking and dense retrieval baselines for the recently released TripClick health ad-hoc retrieval collection.
A Time-Optimized Content Creation Workflow for Remote Teaching
We describe our workflow to create an engaging remote learning experience for a university course, while minimizing the post-production time of the educators.
Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation
Based on this finding, we propose a cross-architecture training procedure with a margin focused loss (Margin-MSE), that adapts knowledge distillation to the varying score output distributions of different BERT and non-BERT passage ranking architectures.
Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering
We extend the ranked retrieval annotations of the Deep Learning track of TREC 2019 with passage and word level graded relevance annotations for all relevant documents.
DEXA: Supporting Non-Expert Annotators with Dynamic Examples from Experts
of 0. 68 to experts in DEXA vs. 0. 40 in CONTROL); (ii) already three per majority voting aggregated annotations of the DEXA approach reach substantial agreements to experts of 0. 78/0. 75/0. 69 for P/I/O (in CONTROL 0. 73/0. 58/0. 46).
