Papers by Gustavo Penha
On the Calibration and Uncertainty of Neural Learning to Rank Models for Conversational Search (2021.eacl-main)
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| Challenge: | Existing methods to rank documents in decreasing order of their probability of relevance are not well calibrated and have several sources of uncertainty. |
| Approach: | They propose to calibrate deterministic neural rankers for conversational search problems . they then use two techniques to model the uncertainty of neural ranker's uncertainty . |
| Outcome: | The proposed rankers output a predictive distribution of relevance as opposed to point estimates. |