Learning Symmetric Collaborative Dialogue Agents with Dynamic Knowledge Graph Embeddings
Table 7: Ablations of our model on the dev set show the importance of entity abstraction and message passing (K=2).
['Model', 'ℓ']
Our model has two novel designs: entity abstraction and message passing for node embeddings. When the number of message passing iterations, K, is reduced from 2 to 0, the loss consistently increases. Removing entity abstraction—meaning adding entity embeddings to node embeddings and the LSTM input embeddings—also degrades performance. This shows that DynoNet benefits from contextually-defined, structural node embeddings rather than ones based on a classic lookup table.