Confidence-Calibrated Ensemble Dense Phrase Retrieval
This incremental improvement enhances retrieval accuracy for question-answering systems, with domain-specific optimizations.
The paper tackled optimizing the Dense Passage Retrieval algorithm without further pre-training by using phrase-length variations and confidence-calibrated ensembles, achieving state-of-the-art results on benchmarks like Google NQ and SQuAD.
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