7 citations · 22 across the 7 of their papers we have counts for
7 papers
RetroMAE-2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models
Shitao Xiao, Zheng Liu, Yingxia Shao +1
To better support information retrieval tasks such as web search and open-domain question answering, growing effort is made to develop retrieval-oriented language models, e.g., Ret…
Generative Retrieval via Term Set Generation
Peitian Zhang, Zheng Liu, Yujia Zhou +3
Recently, generative retrieval emerges as a promising alternative to traditional retrieval paradigms. It assigns each document a unique identifier, known as DocID, and employs a ge…
Constructing Tree-based Index for Efficient and Effective Dense Retrieval
Haitao Li, Qingyao Ai, Jingtao Zhan +4
Recent studies have shown that Dense Retrieval (DR) techniques can significantly improve the performance of first-stage retrieval in IR systems. Despite its empirical effectiveness…
WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus
Hongjing Qian, Yutao Zhu, Zhicheng Dou +7
In this paper, we introduce a new NLP task -- generating short factual articles with references for queries by mining supporting evidence from the Web. In this task, called WebBrai…
Pre-training for Information Retrieval: Are Hyperlinks Fully Explored?
Jiawen Wu, Xinyu Zhang, Yutao Zhu +7
Recent years have witnessed great progress on applying pre-trained language models, e.g., BERT, to information retrieval (IR) tasks. Hyperlinks, which are commonly used in Web page…
Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding
Zhaoye Fei, Yu Tian, Yongkang Wu +9
Generalized text representations are the foundation of many natural language understanding tasks. To fully utilize the different corpus, it is inevitable that models need to unders…
RetroMAE: Pre-Training Retrieval-oriented Language Models Via Masked Auto-Encoder
Shitao Xiao, Zheng Liu, Yingxia Shao +1
Despite pre-training's progress in many important NLP tasks, it remains to explore effective pre-training strategies for dense retrieval. In this paper, we propose RetroMAE, a new…