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Sebastian Hofstätter
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- affiliation: TU Wien, Austria
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2020 – today
- 2024
- [i30]Pat Verga, Sebastian Hofstätter, Sophia Althammer, Yixuan Su, Aleksandra Piktus, Arkady Arkhangorodsky, Minjie Xu, Naomi White, Patrick Lewis:
Replacing Judges with Juries: Evaluating LLM Generations with a Panel of Diverse Models. CoRR abs/2404.18796 (2024) - 2023
- [c32]Mete Sertkan, Sophia Althammer, Sebastian Hofstätter:
Ranger: A Toolkit for Effect-Size Based Multi-Task Evaluation. ACL (demo) 2023: 581-587 - [c31]David Banyasz, Sebastian Hofstätter, Allan Hanbury:
Search in Archival Facsimile Documents for Digital History. e-Science 2023: 1-10 - [c30]Mete Sertkan, Sophia Althammer, Sebastian Hofstätter, Peter Knees, Julia Neidhardt:
Exploring Effect-Size-Based Meta-Analysis for Multi-Dataset Evaluation. Perspectives@RecSys 2023 - [c29]Sebastian Hofstätter
, Jiecao Chen
FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation. SIGIR 2023: 1437-1447 - [c28]Sophia Althammer
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection. SIGIR-AP 2023: 139-149 - [i29]Mete Sertkan, Sophia Althammer, Sebastian Hofstätter:
Ranger: A Toolkit for Effect-Size Based Multi-Task Evaluation. CoRR abs/2305.15048 (2023) - [i28]Sophia Althammer, Guido Zuccon, Sebastian Hofstätter, Suzan Verberne, Allan Hanbury:
Annotating Data for Fine-Tuning a Neural Ranker? Current Active Learning Strategies are not Better than Random Selection. CoRR abs/2309.06131 (2023) - [i27]Xinyu Zhang, Sebastian Hofstätter, Patrick S. H. Lewis, Raphael Tang, Jimmy Lin:
Rank-without-GPT: Building GPT-Independent Listwise Rerankers on Open-Source Large Language Models. CoRR abs/2312.02969 (2023) - 2022
- [c27]Sebastian Hofstätter, Omar Khattab, Sophia Althammer, Mete Sertkan, Allan Hanbury:
Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction. CIKM 2022: 737-747 - [c26]Sophia Althammer, Sebastian Hofstätter, Suzan Verberne
TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval. CIKM 2022: 3801-3805 - [c25]Sophia Althammer, Sebastian Hofstätter, Mete Sertkan, Suzan Verberne
PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval. ECIR (1) 2022: 19-34 - [c24]Sebastian Hofstätter, Sophia Althammer, Mete Sertkan, Allan Hanbury:
Establishing Strong Baselines For TripClick Health Retrieval. ECIR (2) 2022: 144-152 - [c23]Mete Sertkan, Sophia Althammer, Sebastian Hofstätter, Julia Neidhardt:
Diversifying Sentiments in News Recommendation. Perspectives@RecSys 2022 - [c22]Sebastian Hofstätter, Sophia Althammer, Mete Sertkan, Allan Hanbury:
A Time-Optimized Content Creation Workflow for Remote Teaching. SIGCSE (1) 2022: 731-737 - [i26]Sebastian Hofstätter, Sophia Althammer, Mete Sertkan, Allan Hanbury:
Establishing Strong Baselines for TripClick Health Retrieval. CoRR abs/2201.00365 (2022) - [i25]Sophia Althammer, Sebastian Hofstätter, Mete Sertkan, Suzan Verberne, Allan Hanbury:
PARM: A Paragraph Aggregation Retrieval Model for Dense Document-to-Document Retrieval. CoRR abs/2201.01614 (2022) - [i24]Sebastian Hofstätter, Omar Khattab, Sophia Althammer, Mete Sertkan, Allan Hanbury:
Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized Late Interactions using Enhanced Reduction. CoRR abs/2203.13088 (2022) - [i23]Sebastian Hofstätter, Nick Craswell, Bhaskar Mitra, Hamed Zamani, Allan Hanbury:
Are We There Yet? A Decision Framework for Replacing Term Based Retrieval with Dense Retrieval Systems. CoRR abs/2206.12993 (2022) - [i22]Sebastian Hofstätter, Jiecao Chen, Karthik Raman, Hamed Zamani:
Multi-Task Retrieval-Augmented Text Generation with Relevance Sampling. CoRR abs/2207.03030 (2022) - [i21]Sophia Althammer, Sebastian Hofstätter, Suzan Verberne, Allan Hanbury:
TripJudge: A Relevance Judgement Test Collection for TripClick Health Retrieval. CoRR abs/2208.06936 (2022) - [i20]Sebastian Hofstätter, Jiecao Chen, Karthik Raman, Hamed Zamani:
FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation. CoRR abs/2209.14290 (2022) - 2021
- [c21]Sophia Althammer, Sebastian Hofstätter, Allan Hanbury:
Cross-Domain Retrieval in the Legal and Patent Domains: A Reproducibility Study. ECIR (2) 2021: 3-17 - [c20]Sebastian Hofstätter, Aldo Lipani
Mitigating the Position Bias of Transformer Models in Passage Re-ranking. ECIR (1) 2021: 238-253 - [c19]Sebastian Hofstätter, Sheng-Chieh Lin, Jheng-Hong Yang, Jimmy Lin, Allan Hanbury:
Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling. SIGIR 2021: 113-122 - [c18]Sebastian Hofstätter, Bhaskar Mitra, Hamed Zamani, Nick Craswell, Allan Hanbury:
Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking. SIGIR 2021: 1349-1358 - [c17]Bhaskar Mitra, Sebastian Hofstätter, Hamed Zamani, Nick Craswell:
Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence. SIGIR 2021: 1697-1702 - [c16]Sebastian Hofstätter, Mete Sertkan, Allan Hanbury:
TU Wien at TREC DL and Podcast 2021: Simple Compression for Dense Retrieval. TREC 2021 - [i19]Sebastian Hofstätter, Aldo Lipani, Sophia Althammer, Markus Zlabinger, Allan Hanbury:
Mitigating the Position Bias of Transformer Models in Passage Re-Ranking. CoRR abs/2101.06980 (2021) - [i18]Sebastian Hofstätter, Sheng-Chieh Lin, Jheng-Hong Yang, Jimmy Lin, Allan Hanbury:
Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling. CoRR abs/2104.06967 (2021) - [i17]Bhaskar Mitra, Sebastian Hofstätter, Hamed Zamani, Nick Craswell:
Improving Transformer-Kernel Ranking Model Using Conformer and Query Term Independence. CoRR abs/2104.09393 (2021) - [i16]Sebastian Hofstätter, Bhaskar Mitra, Hamed Zamani, Nick Craswell, Allan Hanbury:
Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking. CoRR abs/2105.09816 (2021) - [i15]Sophia Althammer, Mark Buckley, Sebastian Hofstätter, Allan Hanbury:
Linguistically Informed Masking for Representation Learning in the Patent Domain. CoRR abs/2106.05768 (2021) - [i14]Sebastian Hofstätter, Sophia Althammer, Mete Sertkan, Allan Hanbury:
A Time-Optimized Content Creation Workflow for Remote Teaching. CoRR abs/2110.05601 (2021) - 2020
- [c15]Sebastian Hofstätter, Aldo Lipani
Learning to Re-Rank with Contextualized Stopwords. CIKM 2020: 2057-2060 - [c14]Sebastian Hofstätter, Markus Zlabinger, Mete Sertkan, Michael Schröder, Allan Hanbury:
Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering. CIKM 2020: 3031-3038 - [c13]Sebastian Hofstätter, Markus Zlabinger, Allan Hanbury:
Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking. ECAI 2020: 513-520 - [c12]Markus Zlabinger, Sebastian Hofstätter, Navid Rekabsaz, Allan Hanbury:
DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations. ECIR (2) 2020: 433-440 - [c11]Sebastian Hofstätter, Markus Zlabinger, Allan Hanbury:
Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-ranking Results. ECIR (2) 2020: 459-464 - [c10]Markus Zlabinger, Marta Sabou, Sebastian Hofstätter, Allan Hanbury:
Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports. EMNLP (Findings) 2020: 3064-3074 - [c9]Sebastian Hofstätter, Hamed Zamani, Bhaskar Mitra, Nick Craswell, Allan Hanbury:
Local Self-Attention over Long Text for Efficient Document Retrieval. SIGIR 2020: 2021-2024 - [c8]Markus Zlabinger, Marta Sabou, Sebastian Hofstätter, Mete Sertkan, Allan Hanbury:
DEXA: Supporting Non-Expert Annotators with Dynamic Examples from Experts. SIGIR 2020: 2109-2112 - [c7]Sebastian Hofstätter:
End-to-End Contextualized Document Indexing and Retrieval with Neural Networks. SIGIR 2020: 2481 - [c6]Sebastian Hofstätter, Allan Hanbury:
Evaluating Transformer-Kernel Models at TREC Deep Learning 2020. TREC 2020 - [c5]Bhaskar Mitra, Sebastian Hofstätter, Hamed Zamani, Nick Craswell:
Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track. TREC 2020 - [i13]Markus Zlabinger, Sebastian Hofstätter, Navid Rekabsaz, Allan Hanbury:
DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations. CoRR abs/2001.05357 (2020) - [i12]Sebastian Hofstätter, Markus Zlabinger, Allan Hanbury:
Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking. CoRR abs/2002.01854 (2020) - [i11]Sebastian Hofstätter, Hamed Zamani, Bhaskar Mitra, Nick Craswell, Allan Hanbury:
Local Self-Attention over Long Text for Efficient Document Retrieval. CoRR abs/2005.04908 (2020) - [i10]Markus Zlabinger, Marta Sabou, Sebastian Hofstätter, Mete Sertkan, Allan Hanbury:
DEXA: Supporting Non-Expert Annotators with Dynamic Examples from Experts. CoRR abs/2005.08367 (2020) - [i9]Bhaskar Mitra, Sebastian Hofstätter, Hamed Zamani, Nick Craswell:
Conformer-Kernel with Query Term Independence for Document Retrieval. CoRR abs/2007.10434 (2020) - [i8]Sebastian Hofstätter, Markus Zlabinger, Mete Sertkan, Michael Schröder, Allan Hanbury:
Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering. CoRR abs/2008.05363 (2020) - [i7]Sebastian Hofstätter, Sophia Althammer, Michael Schröder, Mete Sertkan, Allan Hanbury:
Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation. CoRR abs/2010.02666 (2020) - [i6]Bhaskar Mitra, Sebastian Hofstätter, Hamed Zamani, Nick Craswell:
Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track. CoRR abs/2011.07368 (2020) - [i5]Sophia Althammer, Sebastian Hofstätter, Allan Hanbury:
Cross-domain Retrieval in the Legal and Patent Domains: a Reproducability Study. CoRR abs/2012.11405 (2020)
2010 – 2019
- 2019
- [c4]Sebastian Hofstätter, Navid Rekabsaz, Mihai Lupu, Carsten Eickhoff, Allan Hanbury:
Enriching Word Embeddings for Patent Retrieval with Global Context. ECIR (1) 2019: 810-818 - [c3]Sebastian Hofstätter, Allan Hanbury:
Let's measure run time! Extending the IR replicability infrastructure to include performance aspects. OSIRRC@SIGIR 2019: 12-16 - [c2]Sebastian Hofstätter, Navid Rekabsaz, Carsten Eickhoff, Allan Hanbury:
On the Effect of Low-Frequency Terms on Neural-IR Models. SIGIR 2019: 1137-1140 - [c1]Sebastian Hofstätter, Markus Zlabinger, Allan Hanbury:
TU Wien @ TREC Deep Learning '19 - Simple Contextualization for Re-ranking. TREC 2019 - [i4]Sebastian Hofstätter, Navid Rekabsaz, Carsten Eickhoff, Allan Hanbury:
On the Effect of Low-Frequency Terms on Neural-IR Models. CoRR abs/1904.12683 (2019) - [i3]Sebastian Hofstätter, Allan Hanbury:
Let's measure run time! Extending the IR replicability infrastructure to include performance aspects. CoRR abs/1907.04614 (2019) - [i2]Sebastian Hofstätter, Markus Zlabinger, Allan Hanbury:
TU Wien @ TREC Deep Learning '19 - Simple Contextualization for Re-ranking. CoRR abs/1912.01385 (2019) - [i1]Sebastian Hofstätter, Markus Zlabinger, Allan Hanbury:
Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-Ranking Results. CoRR abs/1912.04713 (2019)
Coauthor Index
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