Jul 1, 2020 · In our experiments, ANCE boosts the BERT-Siamese DR model to outperform all competitive dense and sparse retrieval baselines. It nearly matches the accuracy of sparse-retrieval-and-BERT-reranking using dot-product in the ANCE-learned representation space and provides almost 100x speed-up.

In our experiments, ANCE boosts the BERT-Siamese DR model to outperform all competitive dense and sparse retrieval baselines. It nearly matches the accuracy of sparse-retrieval-and-BERT-reranking using dot-product in the ANCE-learned representation space and provides almost 100x speed-up.

Oct 22, 2024 · Approximate nearest neighbor Negative Contrastive Estimation (ANCE), which selects negatives from the entire corpus using an asynchronously updated ANN index. ANCE samples negatives...

Jan 12, 2021 · One-sentence Summary: This paper improves the learning of dense text retrieval using ANCE, which selects global negatives with bigger gradient norms using an asynchronously updated ANN index.

demonstrate the advantage of ANCE in three text retrieval scenarios: standard web search (Craswell et al., 2020), OpenQA (Rajpurkar et al., 2016; Kwiatkowski et al., 2019), and in a commercial search engine’s retrieval system. We also empirically validate our theory that the gradient norms on ANCE

This paper presents Approximate nearest neighbor Negative Contrastive Estimation (ANCE), a training mechanism that constructs negatives from an Approximate Nearest Neighbor (ANN) index of the corpus, which is parallelly updated with the learning process to select more realistic negative training instances.

This notebook demonstrates use of PyTerrier plugin for ANCE for dense passage retrieval. ANCE is a dense retrieval system leveraging single representations to encode documents and queries....

We then propose Approximate nearest neighbor Negative Contrastive Learning (ANCE), a learning mechanism that selects hard training negatives globally from the entire corpus, using an asynchronously updated ANN index.

In our experiments, ANCE boosts the BERT-Siamese DR model to outperform all competitive dense and sparse retrieval baselines. It nearly matches the accuracy of sparse-retrieval-and-BERT-reranking using dot-product in the ANCE-learned representation space and provides almost 100x speed-up.

We then propose Approximate nearest neighbor Negative Contrastive Learning (ANCE), a learning mechanism that selects hard training negatives globally from the entire corpus, using an asynchronously updated ANN index.