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MemoryNetworks

  • Jason Weston, Antoine Bordes, Sumit Chopra, Tomas Mikolov, Alexander M. Rush, "Towards AI-Complete Question Answering: A Set of Prerequisite Toy Tasks", http://arxiv.org/abs/1502.05698
  • Sainbayar Sukhbaatar, Arthur Szlam, Jason Weston, Rob Fergus, "End-To-End Memory Networks", http://arxiv.org/abs/1503.08895

Requirements

Results

I tested my model with 10k dataset. () is original MemNNs's performance (1k, 3 hops, PE).

  • Task 1: Acc 100.00% (99.9%)
  • Task 2: Acc 97.78% (78.4%)
  • Task 3: Acc 93.55% (35.8%)
  • Task 4: Acc 78.63% (96.2%) ?
  • Task 5: Acc 91.13% (85.9%)
  • Task 6: Acc 93.55% (92.1%)
  • Task 7: Acc 89.42% (78.4%)
  • Task 8: Acc 95.56% (87.4%)
  • Task 9: Acc 96.77% (76.7%)
  • Task 10: Acc 87.90% (82.6%)
  • Task 11: Acc 94.86% (95.7%)
  • Task 12: Acc 100.00% (99.7%)
  • Task 13: Acc 94.76% (90.1%)
  • Task 14: Acc 100.00% (98.2%)
  • Task 15: Acc 100.00% (100.0%)
  • Task 16: Acc 48.39% (47.9%)
  • Task 17: Acc 52.82% (49.9%)
  • Task 18: Acc 56.65% (86.4%)
  • Task 19: Acc 20.67% (12.6%)
  • Task 20: Acc 100.00% (100.0%)

TODO

  • Random noise (RN)
  • Linear start (LS)
  • joint training
  • compare results with FAIR team (the performance of some tasks is very low)
  • correct optimizer and learning rate

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  • Python 100.0%