DDRQA: Dynamic Document Reranking for Open-domain Multi-hop Question Answering
By Yuyu Zhang, Ping Nie, Arun Ramamurthy, Le Song. 2020
7 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
- Tat-seng Chua
- Yunchang Zhu
- Xueqi Cheng
- Soujanya Poria
- Jianming Zheng
- Zhicheng Dou
- Chao Wang
- Huawei Shen
In Natural Language Processing
- Xueqi Cheng
- Liang Pang
- Yanyan Lan
- Tat-seng Chua
- Yunchang Zhu
- Soujanya Poria
- Jianming Zheng
- Chao Wang
Citations in the corpus
- Adaptive Information Seeking for Open-Domain Question Answering (2021, methodology): “…employing the same retrieval function multiple times (Das et al., 2019a; Feldman and El-Yaniv, 2019; Xiong et al., 2021; Ding et al., 2019; Qi et al., 2019; Zhang et al., 2020; Qi et al., 2020) or pre-designated sequence of applying retrieval functions (Asai et al., 2020; Li et al., 2021).”
- Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering (2021, methodology): “Among the works using Sparse Retriever, GOLDEN Retriever [35] adopts BM25 retrieval, while Graph Retriever [87] and DDRQA [84] retrieve top K documents using TF-IDF.”
- Answer Complex Questions: Path Ranker Is All You Need (2021, methodology): “A good retriever should be able to generate progressive candidate sets [27, 30] and supporting document sequences called paths [1].”