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 applying neural network methods to question answering (QA). These approaches have achieved state of the art results in simplified closed-domain settings such as the SQuAD (Rajpurkar et al. 2016) dataset, which provides a pre-selected pas...