A Neural Corpus Indexer for Document Retrieval
Yujing Wang, Yingyan Hou, Haonan Wang, Ziming Miao, Shibin Wu, Hao Sun, Qi Chen, Yuqing Xia
Abstract
Current state-of-the-art document retrieval solutions mainly follow an index-retrieve paradigm, where the index is hard to be directly optimized for the final retrieval target. In this paper, we aim to show that an end-to-end deep neural network unifying training and indexing stages can significantly improve the recall performance of traditional methods. To this end, we propose Neural Corpus Indexer (NCI), a sequence-to-sequence network that generates relevant document identifiers directly for a designated query. To optimize the recall performance of NCI, we invent a prefix-aware weight-adaptive decoder architecture, and leverage tailored techniques including query generation, semantic document identifiers, and consistency-based regularization. Empirical studies demonstrated the superiority of NCI on two commonly used academic benchmarks, achieving +21.4% and +16.8% relative enhancement for Recall@1 on NQ320k dataset and R-Precision on TriviaQA dataset, respectively, compared to the best baseline method.
BibTeX
@inproceedings{
wang2022a,
title={A Neural Corpus Indexer for Document Retrieval},
author={Yujing Wang and Yingyan Hou and Haonan Wang and Ziming Miao and Shibin Wu and Hao Sun and Qi Chen and Yuqing Xia and Chengmin Chi and Guoshuai Zhao and Zheng Liu and Xing Xie and Weiwei Deng and Qi Zhang and Mao Yang},
booktitle={Advances in Neural Information Processing Systems},
editor={Alice H. Oh and Alekh Agarwal and Danielle Belgrave and Kyunghyun Cho},
year={2022},
url={https://openreview.net/forum?id=fSfcEYQP_qc}
}