Confidence-Calibrated Ensemble Dense Phrase Retrieval
Computation and Language
2023-06-29 v1 Information Retrieval
Abstract
In this paper, we consider the extent to which the transformer-based Dense Passage Retrieval (DPR) algorithm, developed by (Karpukhin et. al. 2020), can be optimized without further pre-training. Our method involves two particular insights: we apply the DPR context encoder at various phrase lengths (e.g. one-sentence versus five-sentence segments), and we take a confidence-calibrated ensemble prediction over all of these different segmentations. This somewhat exhaustive approach achieves start-of-the-art results on benchmark datasets such as Google NQ and SQuAD. We also apply our method to domain-specific datasets, and the results suggest how different granularities are optimal for different domains
Cite
@article{arxiv.2306.15917,
title = {Confidence-Calibrated Ensemble Dense Phrase Retrieval},
author = {William Yang and Noah Bergam and Arnav Jain and Nima Sheikhoslami},
journal= {arXiv preprint arXiv:2306.15917},
year = {2023}
}