Support the Creation of Effective Ad Creatives
Table 5. Comparison of GRU models for creative texts and their attribute value interactions. Performance is improved using conditional attention rather than giving attribute values directly to word vectors.
['Model', 'Model', 'NDCG [%] Single-task', 'NDCG [%]  [BOLD] Multi-task']
In the proposed framework, this interaction is realized with the conditional attention mechanism, explicitly. Because attention is computed by the input variables, this interaction is implicitly expressed by inputting both variables in the text encoder. For confirming the effect of this explicit interaction in the conditional attention mechanism, we compared the model that inputted both variables in the text encoder with the conditional attention mechanism. The conditional attention mechanism showed the best performance in the single-task and multi-task model. Introducing the vanilla model and the conventional attention model to the word representation with ad attribute values resulted in a poor performance, mainly because the duplicate interactions were calculated excessively. It is suggested that it is better to introduce the explicit interaction of attribute values.