68 citations · 177 across the 24 of their papers we have counts for
23 papers · 1 filter
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection
Sophia Althammer, Guido Zuccon, Sebastian Hofstätter +2
Search methods based on Pretrained Language Models (PLM) have demonstrated great effectiveness gains compared to statistical and early neural ranking models. However, fine-tuning P…
TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval
Sophia Althammer, Sebastian Hofstätter, Suzan Verberne +1
Robust test collections are crucial for Information Retrieval research. Recently there is a growing interest in evaluating retrieval systems for domain-specific retrieval tasks, ho…
Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems
Sebastian Hofstätter, Nick Craswell, Bhaskar Mitra +2
Recently, several dense retrieval (DR) models have demonstrated competitive performance to term-based retrieval that are ubiquitous in search systems. In contrast to term-based mat…
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…
PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval
Sophia Althammer, Sebastian Hofstätter, Mete Sertkan +2
Dense passage retrieval (DPR) models show great effectiveness gains in first stage retrieval for the web domain. However in the web domain we are in a setting with large amounts of…
Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling
Sebastian Hofstätter, Sheng-Chieh Lin, Jheng-Hong Yang +2
A vital step towards the widespread adoption of neural retrieval models is their resource efficiency throughout the training, indexing and query workflows. The neural IR community…
Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking
Sebastian Hofstätter, Bhaskar Mitra, Hamed Zamani +2
An emerging recipe for achieving state-of-the-art effectiveness in neural document re-ranking involves utilizing large pre-trained language models - e.g., BERT - to evaluate all in…
Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence
Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani +1
The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark -- and can be considered to be an efficient (but slightly less e…
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility Study
Sophia Althammer, Sebastian Hofstätter, Allan Hanbury
Domain specific search has always been a challenging information retrieval task due to several challenges such as the domain specific language, the unique task setting, as well as…
Mitigating the Position Bias of Transformer Models in Passage Re-Ranking
Sebastian Hofstätter, Aldo Lipani, Sophia Althammer +2
Supervised machine learning models and their evaluation strongly depends on the quality of the underlying dataset. When we search for a relevant piece of information it may appear…
Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track
Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani +1
We benchmark Conformer-Kernel models under the strict blind evaluation setting of the TREC 2020 Deep Learning track. In particular, we study the impact of incorporating: (i) Explic…
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…
Conformer-Kernel with Query Term Independence for Document Retrieval
Bhaskar Mitra, Sebastian Hofstatter, Hamed Zamani +1
The Transformer-Kernel (TK) model has demonstrated strong reranking performance on the TREC Deep Learning benchmark---and can be considered to be an efficient (but slightly less ef…
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…
Local Self-Attention over Long Text for Efficient Document Retrieval
Sebastian Hofstätter, Hamed Zamani, Bhaskar Mitra +2
Neural networks, particularly Transformer-based architectures, have achieved significant performance improvements on several retrieval benchmarks. When the items being retrieved ar…
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…
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…