Titlebook: Advances in Information Retrieval; 44th European Confer Matthias Hagen,Suzan Verberne,Vinay Setty Conference proceedings 2022 The Editor(s)

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Schriftenreihe Neurologie‘ Neurology Series) is so far based on pointwise modeling of individual queries. Meanwhile, recent studies suggest that the cross-attention modeling of a group of documents can effectively boost performances for both learning-to-rank algorithms and BERT-based re-ranking. To this end, a BERT-based groupwise QPP model

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Chronotropic Visions: Conclusionmodels from the perspective of data-to-text generation. We propose the use of a content selection and planning pipeline which aims at structuring the answer by generating intermediate plans. The experimental evaluation is performed using the TREC Complex Answer Retrieval (CAR) dataset. We evaluate b

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https://doi.org/10.1007/978-3-031-32111-5t applications. However, the diversity of sentence embedding techniques poses a challenge, in terms of choosing the model best suited for the downstream task. As such, . study different techniques for combining embeddings from multiple sources. In this paper, we propose ., a . for aggregating variou

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Tommaso De Robertis,Luca Burzelli where transfer learning was beneficial, ignoring the significant trial-and-error required to find effective settings for transfer. Indeed, not all task combinations lead to performance benefits, and brute-force searching rapidly becomes computationally infeasible. Hence the question arises, . In th