Query-Variant Advertisement Text Generation with Association Knowledge
Table 4: Ablation study on the effect of association module and generation module. Our model is built on the basis of Transformer. +GatedGCN means the model includes the input sub-graph constructed with the help of the AKG where there is no associated knowledge, which is encoded with GatedGCN enhanced Transformer encoder. +Association means that the input graph is enhanced with the retrieved external knowledge from the association module. +query (input) means adding query to the input during training. The changes shown in the parentheses are the recall compared with the original human written one.
['Model', 'BLEU', 'Recall(k)', 'Recall(q)', 'Recall(q+k)', 'Dist1', 'Dist2', 'Train', 'Test']
Effect of Association Module: We can see that with the help of the graph structure (+GatedGCN), the BLEU score generally improves. This shows that the graph structure can help improve the model expression ability because not all the keywords are inter-connected with each other. Attending on all the other keywords in Transformer may bring in much noise.