Best results achieved for each model:

Embedding + Max Pooling:
 - Top 1 Precision:
   - 0.492 on test 1
   - 0.483 on test 2
   - 0.495 on dev
 - MRR:
   - 0.624 on test 1
   - 0.611 on test 2
   - 0.624 on dev

Attentional LSTM + Max Pooling:
 - Top 1 precision:
   - 0.480 on test 1
   - 0.465 on test 2
   - 0.487 on dev
 - MRR:
   - 0.627 on test 1
   - 0.613 on test 2
   - 0.635 on dev

Unsupervised RNN language model + trained embeddings:
 - Top 1 precision:
   - 0.546 on test 1
   - 0.527 on test 2
   - 0.552 on dev
 - MRR:
   - 0.670 on test 1
   - 0.651 on test 2
   - 0.671 on dev

Training ConvolutionalLSTM model for a long time (~4 days):
 - Top-1 Precision:
   - 0.564 on test 1
   - 0.543 on test 2
   - 0.573 on dev
 - MRR:
   - 0.681 on test 1
   - 0.661 on test 2
   - 0.686 on dev