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

发酵

Graphite

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

Ophthalmoscope

Angioplasty

scrutiny

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

镀金

疏忽

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

侵略

FUSE

blister

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