30 citations · 67 across the 7 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…