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Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking.

, , и . ECAI, том 325 из Frontiers in Artificial Intelligence and Applications, стр. 513-520. IOS Press, (2020)

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Hans Hofstätter

Anneliese Hofstätter

Gerhard Hofstätter

Michael Hofstätter

Gisela Hofstätter

 

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Linguistically Informed Masking for Representation Learning in the Patent Domain., , , и . CoRR, (2021)Search in Archival Facsimile Documents for Digital History., , и . e-Science, стр. 1-10. IEEE, (2023)Conformer-Kernel with Query Term Independence at TREC 2020 Deep Learning Track., , , и . TREC, том 1266 из NIST Special Publication, National Institute of Standards and Technology (NIST), (2020)Intra-Document Cascading: Learning to Select Passages for Neural Document Ranking., , , , и . SIGIR, стр. 1349-1358. ACM, (2021)End-to-End Contextualized Document Indexing and Retrieval with Neural Networks.. SIGIR, стр. 2481. ACM, (2020)DEXA: Supporting Non-Expert Annotators with Dynamic Examples from Experts., , , , и . SIGIR, стр. 2109-2112. ACM, (2020)FiD-Light: Efficient and Effective Retrieval-Augmented Text Generation., , , и . SIGIR, стр. 1437-1447. ACM, (2023)Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling., , , , и . SIGIR, стр. 113-122. ACM, (2021)Interpretable & Time-Budget-Constrained Contextualization for Re-Ranking., , и . ECAI, том 325 из Frontiers in Artificial Intelligence and Applications, стр. 513-520. IOS Press, (2020)Evaluating Transformer-Kernel Models at TREC Deep Learning 2020., и . TREC, том 1266 из NIST Special Publication, National Institute of Standards and Technology (NIST), (2020)