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<div>Third Call for Papers - ACM Transactions on Information Systems</div>
<div>Special Section on Efficiency in Neural Information Retrieval</div>
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<div>Full Call of Papers: https://dl.acm.org/journal/tois/calls-for-papers</div>
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<div>Overview 🧐</div>
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<div>The aim of this Special Section is to engage with researchers in Information Retrieval, Natural Language Processing, and related areas and gather insight into the core challenges in measuring, reporting, and optimizing all facets of efficiency in Neural
Information Retrieval (NIR) systems, including time-, space-, resource-, sample- and energy- efficiency, among other factors.</div>
<div>This special section solicits perspectives from active researchers to advance our understanding of and to overcome efficiency challenges in NIR. </div>
<div>In particular, researchers are encouraged to examine the ever-growing model complexity through appropriate empirical analysis, to propose models that require less data, computational resources, and energy for training and fine-tuning with similarly efficient
inference, to ask if there are meaningful simplifications of the existing training processes or model architectures that lead to comparable quality, and explore a multi-faceted evaluation of NIR models from quality to all dimensions of efficiency with standardized
metrics.</div>
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<div>Topics 🔍</div>
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<div>We welcome submissions on the following topics, including but not limited to:</div>
<div>* Novel NIR models that reach competitive quality but are designed to provide efficient training or</div>
<div>inference;</div>
<div>* Efficient NIR models for decentralized IR tasks such as conversational search;</div>
<div>* Efficient NIR models for IR-related tasks such as question answering and recommender systems;</div>
<div>* Efficient NIR for resource-constrained devices;</div>
<div>* Scalability of NIR systems;</div>
<div>* Efficient NIR for text and cross-modal search;</div>
<div>* Strategies to optimize training or inference of existing NIR models;</div>
<div>* Sample-efficient training of NIR models;</div>
<div>* Efficiency-driven distillation, pruning, quantization, retraining, and transfer learning;</div>
<div>* Empirical investigation of the complexity of existing NIR models through an analysis of quality, interpretability, robustness, and environmental impact;</div>
<div>* Evaluation protocols for efficiency in NIR.</div>
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<div>Important Dates 🔥</div>
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<div>* Open for Submissions: Aug 1, 2022</div>
<div>* Submissions deadline: Feb 28, 2023</div>
<div>* First-round review decisions: May 31, 2023</div>
<div>* Deadline for minor revision submissions: Jun 30, 2023</div>
<div>* Deadline for major revision submissions: Aug 31, 2023</div>
<div>* Notification of final decisions: Sept 30, 2023</div>
<div>* Tentative publication: 2024</div>
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<div>Guest Editors 📚</div>
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<div>* Dr. Sebastian Bruch, Pinecone, United States of America</div>
<div>* Prof. Claudio Lucchese, Ca' Foscari University of Venice, Italy</div>
<div>* Dr. Maria Maistro, University of Copenhagen, Denmark</div>
<div>* Dr. Franco Maria Nardini, ISTI-CNR, Italy</div>
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<div>———<br>
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Maria Maistro, PhD<br>
Tenure-track Assistant Professor<br>
Department of Computer Science<br>
University of Copenhagen<br>
Universitetsparken 5, 2100 Copenhagen, Denmark</div>
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