- The reported experimental results cannot strongly demonstrate the effectiveness of the proposed method.
- In Table 1, for the proposed method, only 6 of the total 14 evaluation metrics achieve SOTA performances.
- In Table 2, for the proposed method, only 8 of the total 14 evaluation metrics achieve SOTA performances. In addition, under the setting of "Twitter-2017 $\rightarrow$ Twitter-2015", why the proposed method achieves best overall F1, while not achieves best F1 in all single types?
- In Table 3, for the proposed method, 9 of the total 14 evaluation metrics achieve SOTA performances, which means that when ablating some modules, the performance of the proposed method will improve. Furthermore, The performance improvement that adding a certain module can bring is not obvious.
- In line 284, a transformer layer with self-attention is used to capture the intra-modality relation for the test modality. However, there're a lot of self-attention transformer layers in BERT. Why not using the attention scores in the last self-attention transformer layer?
- In line 322, softmmax -> softmax
- Will the coordination of $b_d$ exceed the scope of the patches?
- In Table 2, for the proposed method, only 8 of the total 14 evaluation metrics achieve SOTA performances. In addition, under the setting of "Twitter-2017 $\rightarrow$ Twitter-2015", why the proposed method achieves best overall F1, while not achieves best F1 in all single types?