170 citations · 387 across the 17 of their papers we have counts for
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cs.IR2020
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…
cs.IR2020
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…
cs.IR2020★ 59 cited
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…