387 citations · 592 across the 31 of their papers we have counts for
39 papers
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…
CRUISE-Screening: Living Literature Reviews Toolbox
Wojciech Kusa, Petr Knoth, Allan Hanbury
Keeping up with research and finding related work is still a time-consuming task for academics. Researchers sift through thousands of studies to identify a few relevant ones. Autom…
Outcome-based Evaluation of Systematic Review Automation
Wojciech Kusa, Guido Zuccon, Petr Knoth +1
Current methods of evaluating search strategies and automated citation screening for systematic literature reviews typically rely on counting the number of relevant and not relevan…
Statute-enhanced lexical retrieval of court cases for COLIEE 2022
Tobias Fink, Gabor Recski, Wojciech Kusa +1
We discuss our experiments for COLIEE Task 1, a court case retrieval competition using cases from the Federal Court of Canada. During experiments on the training data we observe th…
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…
Identifying the root cause of cable network problems with machine learning
Georg Heiler, Thassilo Gadermaier, Thomas Haider +2
Good quality network connectivity is ever more important. For hybrid fiber coaxial (HFC) networks, searching for upstream high noise in the past was cumbersome and time-consuming.…
Automation of Citation Screening for Systematic Literature Reviews using Neural Networks: A Replicability Study
Wojciech Kusa, Allan Hanbury, Petr Knoth
In the process of Systematic Literature Review, citation screening is estimated to be one of the most time-consuming steps. Multiple approaches to automate it using various machine…
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…
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…
DoSSIER@COLIEE 2021: Leveraging dense retrieval and summarization-based re-ranking for case law retrieval
Sophia Althammer, Arian Askari, Suzan Verberne +1
In this paper, we present our approaches for the case law retrieval and the legal case entailment task in the Competition on Legal Information Extraction/Entailment (COLIEE) 2021.…
Linguistically Informed Masking for Representation Learning in the Patent Domain
Sophia Althammer, Mark Buckley, Sebastian Hofstätter +1
Domain-specific contextualized language models have demonstrated substantial effectiveness gains for domain-specific downstream tasks, like similarity matching, entity recognition…
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…
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…
Behavioral gender differences are reinforced during the COVID-19 crisis
Tobias Reisch, Georg Heiler, Jan Hurt +3
Behavioral gender differences are known to exist for a wide range of human activities including the way people communicate, move, provision themselves, or organize leisure activiti…
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…
The impact of COVID-19 on relative changes in aggregated mobility using mobile-phone data
Georg Heiler, Allan Hanbury, Peter Filzmoser
Evaluating relative changes leads to additional insights which would remain hidden when only evaluating absolute changes. We analyze a dataset describing mobility of mobile phones…
Country-wide mobility changes observed using mobile phone data during COVID-19 pandemic
Georg Heiler, Tobias Reisch, Jan Hurt +4
In March 2020, the Austrian government introduced a widespread lock-down in response to the COVID-19 pandemic. Based on subjective impressions and anecdotal evidence, Austrian publ…
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…