52 citations · 85 across the 5 of their papers we have counts for
5 papers
Moving Stuff Around: A study on efficiency of moving documents into memory for Neural IR models
Arthur Câmara, Claudia Hauff
When training neural rankers using Large Language Models, it's expected that a practitioner would make use of multiple GPUs to accelerate the training time. By using more devices,…
Searching, Learning, and Subtopic Ordering: A Simulation-based Analysis
Arthur Câmara, David Maxwell, Claudia Hauff
Complex search tasks - such as those from the Search as Learning (SAL) domain - often result in users developing an information need composed of several aspects. However, current m…
Diagnosing BERT with Retrieval Heuristics
Arthur Câmara, Claudia Hauff
Word embeddings, made widely popular in 2013 with the release of word2vec, have become a mainstay of NLP engineering pipelines. Recently, with the release of BERT, word embeddings…
Searching to Learn with Instructional Scaffolding
Arthur Câmara, Nirmal Roy, David Maxwell +1
Search engines are considered the primary tool to assist and empower learners in finding information relevant to their learning goals-be it learning something new, improving their…
Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators
Gustavo Penha, Arthur Câmara, Claudia Hauff
Heavily pre-trained transformers for language modelling, such as BERT, have shown to be remarkably effective for Information Retrieval (IR) tasks, typically applied to re-rank the…