Publications
Hofstätter, S., Zamani, H., Mitra, B., Craswell, N. (2020) Local Self-Attention over Long Text for Efficient Document Retrieval, Association for Computing Machinery, New York, NY, United States, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.
Hofstätter, S., Zlabinger, M., Hanbury, A. (2020) Interpretable & time-budget-constrained contextualization for re-ranking, IOS Press, ECAI 2020 24th European Conference on Artificial Intelligence, 29 August-8 September 2020, Santiago de Compostela, Spain - Including 10th Conference on Prestigious Applications of Artificial Intelligence (PAIS 2020).
Hofstätter, S., Zlabinger, M., Hanbury, A. (2020) Neural-IR-Explorer: A Content-Focused Tool to Explore Neural Re-ranking Results, Springer, Advances in Information Retrieval 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14-17, 2020, Proceedings, Part II.
Hofstätter, S., Zlabinger, M., Sertkan, M., Schröder, M. (2020) Fine-Grained Relevance Annotations for Multi-Task Document Ranking and Question Answering, Association for Computing Machinery, Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 
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Mitra, B., Hofstätter, S., Zamani, H., Craswell, N. (2020) Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track, NIST Special Publication: The Twenty-Ninth Text REtrieval Conference Proceedings (TREC 2020).
Zlabinger, M., Sabou, R., Hofstätter, S., Sertkan, M. (2020) DEXA: Supporting Non-Expert Annotators with Dynamic Examples from Experts, Association for Computing Machinery, New York, NY, United States, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.
Hofstätter, S., Althammer, S., Schröder, M., Sertkan, M. (2020) Improving Efficient Neural Ranking Models with Cross-Architecture Knowledge Distillation, ArXiv, 2010.02666-.
Mitra, B., Hofstätter, S., Zamani, H., Craswell, N. (2020) Conformer-Kernel with Query Term Independence for Document Retrieval, ArXiv, 2007.10434-.
Zlabinger, M., Hofstätter, S., Rekabsaz, N., Hanbury, A. (2020) DSR: A Collection for the Evaluation of Graded Disease-Symptom Relations, Springer Nature Switzerland AG 2021, Advances in Information Retrieval 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14-17, 2020, Proceedings, Part II.
Zlabinger, M., Sabou, R., Hofstätter, S., Hanbury, A. (2020) Effective Crowd-Annotation of Participants, Interventions, and Outcomes in the Text of Clinical Trial Reports, The Association for Computational Linguistics, Findings of the Association for Computational Linguistics: EMNLP 2020.
Hofstätter, S. (2020) End-to-End Contextualized Document Indexing and Retrieval with Neural Networks, Association for Computing Machinery, New York, NY, United States, SIGIR '20: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval.
Hofstätter, S., Hanbury, A. (2020) Evaluating Transformer-Kernel Models at TREC Deep Learning 2020, NIST Special Publication: The Twenty-Ninth Text REtrieval Conference Proceedings (TREC 2020).
Hofstätter, S., Lipani, A., Zlabinger, M., Hanbury, A. (2020) Learning to Re-Rank with Contextualized Stopwords, Proceedings of the 29th ACM International Conference on Information & Knowledge Management.
Hofstätter, S., Rekabsaz, N., Lupu, M., Eickhoff, C. (2019) Enriching Word Embeddings for Patent Retrieval with Global Context, Springer, Cham, Advances in Information Retrieval. 41st European Conference on IR Research, ECIR 2019, Cologne, Germany, April 14-18, 2019, Proceedings, Part I.
Hofstätter, S., Hanbury, A. (2019) Let's measure run time! Extending the IR replicability infrastructure to include performance aspects, Proceedings of the Open-Source IR Replicability Challenge (OSIRRC 2019) co-located with 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019).
Hofstätter, S., Rekabsaz, N., Eickhoff, C., Hanbury, A. (2019) On the Effect of Low-Frequency Terms on Neural-IR Models, Association for Computing Machinery, SIGIR'19: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval.
Hofstätter, S. (2018) Adaptierung von Word Embeddings für domänenspezifisches Information Retrieval, , Master thesis at Institut für Information Systems Engineering.