# A Neural Passage Model for Ad-hoc Document Retrieval

arXiv: https://arxiv.org/abs/2103.09306 · HTML: https://mlsanity.com/paper/2103.09306/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Mar 2021.

## Built on by (most cited first, 1 of 1)

- [A Neural Model for Joint Document and Snippet Ranking in Question Answering for Large Document Collections](https://mlsanity.com/paper/2106.08908/) (2021)

## Abstract (arXiv)

Traditional statistical retrieval models often treat each document as a whole. In many cases, however, a document is relevant to a query only because a small part of it contain the targeted information. In this work, we propose a neural passage model (NPM) that uses passage-level information to improve the performance of ad-hoc retrieval. Instead of using a single window to extract passages, our model automatically learns to weight passages with different granularities in the training process. We show that the passage-based document ranking paradigm from previous studies can be directly derived from our neural framework. Also, our experiments on a TREC collection showed that the NPM can significantly outperform the existing passage-based retrieval models.