17 citations · 21 across the 3 of their papers we have counts for
6 papers
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…
Slice-Aware Neural Ranking
Gustavo Penha, Claudia Hauff
Understanding when and why neural ranking models fail for an IR task via error analysis is an important part of the research cycle. Here we focus on the challenges of (i) identifyi…
What does BERT know about books, movies and music? Probing BERT for Conversational Recommendation
Gustavo Penha, Claudia Hauff
Heavily pre-trained transformer models such as BERT have recently shown to be remarkably powerful at language modelling by achieving impressive results on numerous downstream tasks…
Curriculum Learning Strategies for IR: An Empirical Study on Conversation Response Ranking
Gustavo Penha, Claudia Hauff
Neural ranking models are traditionally trained on a series of random batches, sampled uniformly from the entire training set. Curriculum learning has recently been shown to improv…
Introducing MANtIS: a novel Multi-Domain Information Seeking Dialogues Dataset
Gustavo Penha, Alexandru Balan, Claudia Hauff
Conversational search is an approach to information retrieval (IR), where users engage in a dialogue with an agent in order to satisfy their information needs. Previous conceptual…