Contextualized Sparse Representation with Rectified N-Gram Attention for Open-Domain Question Answering
By Jinhyuk Lee, Minjoon Seo, Hannaneh Hajishirzi, Jaewoo Kang. 2019
53 citations recorded by the source corpus.
Subfields: Information Retrieval & Search, Natural Language Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Retrieval & Search
- Jinhyuk Lee
- Danqi Chen
- Hannaneh Hajishirzi
- Jaewoo Kang
- Minjoon Seo
- Jianfeng Gao
- Yelong Shen
- Wen-tau Yih
In Natural Language Processing
- Jianfeng Gao
- Hannaneh Hajishirzi
- Luke S. Zettlemoyer
- Weizhu Chen
- Xiaodong Liu
- Mohit Bansal
- Danqi Chen
- Richard Socher
Citations in the corpus
- Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index (2019, background): “See Prager et al. (2007) for a comprehensive overview of early work.”
- Learning Dense Representations of Phrases at Scale (2020, background): “Open-domain question answering (QA) aims to provide answers to natural-language questions us-ing a large text corpus (Voorhees et al., 1999; Fer-rucci et al., 2010; Chen and Yih, 2020).”
- UnitedQA: A Hybrid Approach for Open Domain Question Answering (2021, background): “Open-domain question answering (QA) has been a long standing problem in natural language understanding, information retrieval, and related fields (Chen and Yih, 2020).”
- Document Gated Reader for Open-Domain Question Answering (2019, background): “Open domain question answering (ODQA) is a long-term research interest in the NLP community [20, 37].”