59 citations · 83 across the 8 of their papers we have counts for
10 papers
Improving Content Retrievability in Search with Controllable Query Generation
Gustavo Penha, Enrico Palumbo, Maryam Aziz +2
An important goal of online platforms is to enable content discovery, i.e. allow users to find a catalog entity they were not familiar with. A pre-requisite to discover an entity,…
Do the Findings of Document and Passage Retrieval Generalize to the Retrieval of Responses for Dialogues?
Gustavo Penha, Claudia Hauff
A number of learned sparse and dense retrieval approaches have recently been proposed and proven effective in tasks such as passage retrieval and document retrieval. In this paper…
Sparse and Dense Approaches for the Full-rank Retrieval of Responses for Dialogues
Gustavo Penha, Claudia Hauff
Ranking responses for a given dialogue context is a popular benchmark in which the setup is to re-rank the ground-truth response over a limited set of responses, where is t…
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…
On the Calibration and Uncertainty of Neural Learning to Rank Models
Gustavo Penha, Claudia Hauff
According to the Probability Ranking Principle (PRP), ranking documents in decreasing order of their probability of relevance leads to an optimal document ranking for ad-hoc retrie…
Weakly Supervised Label Smoothing
Gustavo Penha, Claudia Hauff
We study Label Smoothing (LS), a widely used regularization technique, in the context of neural learning to rank (L2R) models. LS combines the ground-truth labels with a uniform di…