R3: Reinforced Ranker-Reader for open-domain Question Answering

• Main Authors: WANG, Shuohang, YU, Mo, GUO, Xiaoxiao, WANG, Zhiguo, KLINGER, Tim, ZHANG, Wei, CHANG, Shiyu, TESAURO, Gerald, ZHOU, Bowen, JIANG, Jing
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Answer extraction; Ground truth; Neural network method; Open domain question answering; Question Answering; Reading comprehension; Databases and Information Systems; Theory and Algorithms
• Online Access: https://ink.library.smu.edu.sg/sis_research/4237; https://ink.library.smu.edu.sg/context/sis_research/article/5240/viewcontent/Reinforced_Reader_Ranker_2018_AAAI_afv.pdf
• Institution: Singapore Management University

In recent years researchers have achieved considerable success applyingneural network methods to question answering (QA). These approaches haveachieved state of the art results in simplified closed-domain settings such asthe SQuAD (Rajpurkar et al., 2016) dataset, which provides a pre-selectedpassag...