Search Results for author:
Found 5 papers, 2 papers with code
Benchmarking Middle-Trained Language Models for Neural Search
We compare both dense and sparse approaches under various finetuning protocols and middle training on different collections (MS MARCO, Wikipedia or Tripclick).
A Static Pruning Study on Sparse Neural Retrievers
Sparse neural retrievers, such as DeepImpact, uniCOIL and SPLADE, have been introduced recently as an efficient and effective way to perform retrieval with inverted indexes.
A Study on FGSM Adversarial Training for Neural Retrieval
Neural retrieval models have acquired significant effectiveness gains over the last few years compared to term-based methods.
MS-Shift: An Analysis of MS MARCO Distribution Shifts on Neural Retrieval
To this end, we build three query-based distribution shifts within MS MARCO (query-semantic, query-intent, query-length), which are used to evaluate the three main families of neural retrievers based on BERT: sparse, dense, and late-interaction -- as well as a monoBERT re-ranker.
Zero-Shot and Few-Shot Classification of Biomedical Articles in Context of the COVID-19 Pandemic
MeSH (Medical Subject Headings) is a large thesaurus created by the National Library of Medicine and used for fine-grained indexing of publications in the biomedical domain.
