Learning to Respond with Stickers: A Framework of Unifying Multi-Modality in Multi-Turn Dialog
Table 3. RQ1: Automatic evaluation comparison. Significant differences are with respect to MRFN.
['[EMPTY]', 'MAP', '[ITALIC] R10@1', '[ITALIC] R10@2', '[ITALIC] R10@5']
First, the performance of the multi-turn response selection models is generally consistent with their performances on text response selection datasets. SMN ( , obtains the worst performance on both sticker response and text response selection. MRFN is the state-of-the-art text response selection model and achieves the best performance among baselines in our task as well. Second, VQA models perform generally worse than multi-turn response selection models, since the interaction between multi-turn utterances and sticker is important, which is not taken into account by VQA models. Finally, SRS achieves the best performance with 3.36%, 5.92% and 3.72% improvements in MAP, R10@1 and R10@2 respectively, over the state-of-the-art multi-turn selection model, i.e. ,MRFN, and with 6.80%, 10.69% and 8.74% significant increases over the state-of-the-art visual dialog model, PSAC. This proves the superiority of our model.