Query-Reduction Networksfor Question Answering
Table 2: bAbI QA dataset [Weston et al., 2016] error rates (%) of QRN and previous work: LSTM [Weston et al., 2016], End-to-end Memory Networks (N2N) [Sukhbaatar et al., 2015], Dynamic Memory Networks (DMN+) [Xiong et al., 2016], Gated End-to-end Memory Networks(GMemN2N) [Perez and Liu, 2016]. Results within each task of Differentiable Neural Computer(DNC) were not provided in its paper Graves et al. [2016]). For QRN, a number in the front (1, 2, 3, 6) indicates the number of layers. A number in the back (200) indicates the dimension of hidden vector, while the default value is 50. ‘r’ indicates that the reset gate is used, and ‘v’ indicates that the gates were vectorized. ‘*’ indicates joint training.
['Task', '1k Previous works', '1k Previous works', '1k Previous works', '1k Previous works', '1k QRN', '1k QRN', '1k QRN', '1k QRN', '1k QRN', '1k QRN', '10k Previous works', '10k Previous works', '10k Previous works', '10k QRN', '10k QRN', '10k QRN', '10k QRN']
In 1k data, QRN’s ‘2r’ (2 layers + reset gate + d=50) outperforms all other models by a large margin (2.8+%). In 10k dataset, the average accuracy of QRN’s ‘6r200’ (6 layers + reset gate + d=200) model outperforms all previous models by a large margin (2.5+%), achieving a nearly perfect score of 99.7%.