End-To-End Memory Networks

NeurIPS 2015 Sainbayar SukhbaatarArthur SzlamJason WestonRob Fergus

We introduce a neural network with a recurrent attention model over a possibly large external memory. The architecture is a form of Memory Network (Weston et al., 2015) but unlike the model in that work, it is trained end-to-end, and hence requires significantly less supervision during training, making it more generally applicable in realistic settings... (read more)

PDF Abstract

Evaluation results from the paper


Task Dataset Model Metric name Metric value Global rank Compare
Question Answering bAbi End-To-End Memory Networks Accuracy (trained on 10k) 93.4% # 4
Question Answering bAbi End-To-End Memory Networks Accuracy (trained on 1k) 86.1% # 3
Question Answering bAbi End-To-End Memory Networks Mean Error Rate 7.5% # 5