Query-Variant Advertisement Text Generation with Association Knowledge
Table 5: Effect of different retrieving methods. ”Random” means randomly selecting ϕ neighbors from AKG. ”PMI” means selecting neighbors with top-ϕ PMI scores. ”no-filter” means selecting all the neighbors in AKG. “Proposal” means selecting with our proposed association module. 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 Different Retrieval Methods: Among all the retrieving methods, our proposed method achieves the best on all the metrics. Even though the ”no-filter” method achieves relatively better BLEU and recall rate (without query) than the other two, the improvement of recall(q+k) is the lowest (+13.12). We assume that this is because too much noise is brought by adding all the neighbors in AKG to the input sub-graph. ” PMI” method achieves higher recall improvement (+13.84), which is the sign of the importance of the candidate knowledge. However, the PMI score focuses on the global knowledge, while our method not only makes use of the PMI score via the edge weight in the GCN component in the association module but also considers the semantics of the keywords encoded in the graph representations. This design makes our method achieve the highest recall improvement (+15.48) among all the retrieval methods.