Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog
Table 4. RQ2: Evaluation of different ablation models.
['[EMPTY]', 'MAP', '[ITALIC] R10@1', '[ITALIC] R10@2', '[ITALIC] R10@5']
For research question RQ2, we conduct ablation tests on the use of the pre-trained Inception-v3 model, the sticker classification loss, the deep interaction network and the fusion RNN respectively. The performances of all ablation models are worse than that of SRS under all metrics, which demonstrates the necessity of each component in SRS. We also find that the sticker classification makes the least contribution to the overall performance. But this additional task can speed up the training process, and help our model to converge quickly. We use 19 hours to train the SRS until convergence, and we use 30 hours for training SRS w/o Classify. The fusion RNN brings a significant contribution, improving the MAP and R10@1 scores by 4.43% and 7.08%, respectively. Besides, the deep interaction network also plays an important part. Without this module, the interaction between the sticker and utterance are hindered, leading to a 6.88% drop in R10@1.