Sebastian Hofstätter

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research

∙ 09/12/2023

Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection

Search methods based on Pretrained Language Models (PLM) have demonstrat...

0 Sophia Althammer, et al. ∙

research

∙ 05/24/2023

Ranger: A Toolkit for Effect-Size Based Multi-Task Evaluation

In this paper, we introduce Ranger - a toolkit to facilitate the easy us...

9 Mete Sertkan, et al. ∙

research

∙ 09/28/2022

FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation

Retrieval-augmented generation models offer many benefits over standalon...

0 Sebastian Hofstätter, et al. ∙

research

∙ 08/14/2022

TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval

Robust test collections are crucial for Information Retrieval research. ...

0 Sophia Althammer, et al. ∙

research

∙ 07/07/2022

Multi-Task Retrieval-Augmented Text Generation with Relevance Sampling

This paper studies multi-task training of retrieval-augmented generation...

0 Sebastian Hofstätter, et al. ∙

research

∙ 06/26/2022

Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems

Recently, several dense retrieval (DR) models have demonstrated competit...

0 Sebastian Hofstätter, et al. ∙

research

∙ 03/24/2022

Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction

Recent progress in neural information retrieval has demonstrated large g...

42 Sebastian Hofstätter, et al. ∙

research

∙ 01/05/2022

PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval

Dense passage retrieval (DPR) models show great effectiveness gains in f...

0 Sophia Althammer, et al. ∙

research

∙ 01/02/2022

Establishing Strong Baselines for TripClick Health Retrieval

We present strong Transformer-based re-ranking and dense retrieval basel...

3 Sebastian Hofstätter, et al. ∙

research

∙ 10/11/2021

A Time-Optimized Content Creation Workflow for Remote Teaching

We describe our workflow to create an engaging remote learning experienc...

0 Sebastian Hofstätter, et al. ∙

research

∙ 06/10/2021

Linguistically Informed Masking for Representation Learning in the Patent Domain

Domain-specific contextualized language models have demonstrated substan...

1 Sophia Althammer, et al. ∙

research

∙ 05/20/2021

Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking

An emerging recipe for achieving state-of-the-art effectiveness in neura...

0 Sebastian Hofstätter, et al. ∙

research

∙ 04/19/2021

Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence

The Transformer-Kernel (TK) model has demonstrated strong reranking perf...

0 Bhaskar Mitra, et al. ∙

research

∙ 04/14/2021

Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling

A vital step towards the widespread adoption of neural retrieval models ...

0 Sebastian Hofstätter, et al. ∙

research

∙ 01/18/2021

Mitigating the Position Bias of Transformer Models in Passage Re-Ranking

Supervised machine learning models and their evaluation strongly depends...

0 Sebastian Hofstätter, et al. ∙

research

∙ 12/21/2020

Cross-domain Retrieval in the Legal and Patent Domains: a Reproducibility Study

Domain specific search has always been a challenging information retriev...

0 Sophia Althammer, et al. ∙

research

∙ 11/14/2020

Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track

We benchmark Conformer-Kernel models under the strict blind evaluation s...

0 Bhaskar Mitra, et al. ∙

research

∙ 10/06/2020

Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation

The latency of neural ranking models at query time is largely dependent ...

0 Sebastian Hofstätter, et al. ∙

research

∙ 08/12/2020

Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering

There are many existing retrieval and question answering datasets. Howev...

0 Sebastian Hofstätter, et al. ∙

research

∙ 07/20/2020

Conformer-Kernel with Query Term Independence for Document Retrieval

The Transformer-Kernel (TK) model has demonstrated strong reranking perf...

0 Bhaskar Mitra, et al. ∙

research

∙ 05/17/2020

DEXA: Supporting Non-Expert Annotators with Dynamic Examples from Experts

The success of crowdsourcing based annotation of text corpora depends on...

0 Markus Zlabinger, et al. ∙

research

∙ 05/11/2020

Local Self-Attention over Long Text for Efficient Document Retrieval

Neural networks, particularly Transformer-based architectures, have achi...

0 Sebastian Hofstätter, et al. ∙

research

∙ 02/04/2020

Interpretable Time-Budget-Constrained Contextualization for Re-Ranking

Search engines operate under a strict time constraint as a fast response...

0 Sebastian Hofstätter, et al. ∙

research

∙ 01/15/2020

DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations

The effective extraction of ranked disease-symptom relationships is a cr...

0 Markus Zlabinger, et al. ∙

research

∙ 12/10/2019

Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-Ranking Results

In this paper we look beyond metrics-based evaluation of Information Ret...

0 Sebastian Hofstätter, et al. ∙

research

∙ 12/03/2019

TU Wien @ TREC Deep Learning '19 – Simple Contextualization for Re-ranking

The usage of neural network models puts multiple objectives in conflict ...

0 Sebastian Hofstätter, et al. ∙

research

∙ 07/10/2019

Let's measure run time! Extending the IR replicability infrastructure to include performance aspects

Establishing a docker-based replicability infrastructure offers the comm...

0 Sebastian Hofstätter, et al. ∙

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