Mastering the Dungeon: Grounded Language Learning by Mechanical Turker Descent
Table 6: Ablation study on model variants.
['Model', 'Accuracy', 'F1']
We remove the counter feature counta,j and the location embedding locationj subsequently from AC-Seq2Seq and evaluate the performance. Maintaining a counter of previous arguments contributes substantially to our final performance, which indicates the importance of a compositional action representation. However, encoding the location information is not beneficial, which suggests that the agent has not yet learned to utilize such information. Lastly, even without the counter, AC-Seq2Seq still substantially outperforms Seq2Seq, demonstrating the effectiveness of our action-centric decoder architecture.