# Research on Applying the \"Shift\" Concept to Deep Attention Matching
## 4 Experiments
### Comparison Methods
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Ubuntu Dialogue Corpus**} & \multicolumn{4}{c}{**Douban Conversation Corpus**} \\ \cline{2-11}  & \(R_{2}\)@1 & \(R_{10}\)@1 & \(R_{10}\)@2 & \(R_{10}\)@5 & \(MAP\) & \(MRR\) & \(P\)@1 & \(R_{10}\)@1 & \(R_{10}\)@2 & \(R_{10}\)@5 \\ \hline SMN & 0.926 & 0.726 & 0.847 & 0.961 & 0.529 & 0.569 & 0.397 & 0.233 & 0.396 & 0.724 \\ \hline DAM & 0.938 & 0.767 & 0.847 & 0.969 & 0.550 & 0.601 & 0.427 & 0.254 & 0.410 & 0.757 \\ \hline SMHA & 0.941 & 0.773 & 0.879 & 0.972 & 0.560 & 0.604 & 0.429 & 0.254 & 0.422 & 0.773 \\ \hline Ours-1 & 0.941 & 0.776 & 0.881 & 0.970 & 0.558 & 0.604 & 0.431 & 0.257 & 0.425 & 0.769 \\ \hline
1 to 3 & 0.945 & 0.780 & 0.884 & 0.972 & 0.560 & 0.604 & 0.436 & 0.260 & 0.420 & 0.770 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The results of the comparison experiment for the two proposed methods. cross-attention matching matrices are selected. We remove the self-attention matching matrices and the cross-attention matching matrices in the matching phase and keep only the new matrices, i.e., the final aggregated matrices are only formed by the new method proposed in this paper. As shown in Table 8, the results of the model \(M_{shift}\) (using only the shifted granularity matching matrix) are worse than those of the model with only the self-attentive matching matrix or the cross-attentive matching matrix but are not so different as to suggest that the method is effective in extracting the semantic information and that the information extracted using this method is different from that extracted using the other two matching matrices. From the results of the comparison can be known that \(M_{shift}\) contains the supplementary information required in the original model. \(M_{shift-cross}\) indicates the ablation results of the multi-level shifted cross-attention granularity method. Comparing the results of the other two ablation experiments with the original model, it can be seen that the results are very close to those of DAM and better than those of the other two matching matrices. We believe there may be several reasons:  
1. The multi-level shifted cross-attention granularity matching method can mine more information than that of the other two matching methods;
2. The method can cover part of the information that can be mined using the cross-attention and self-attention matching, for example, there may be some overlap between them, but fails to be better than the full DAM in terms of results as it has not completely replaced both the matching methods;
3. \(M_{shift}\) and \(M_{cross}\) are subject to interaction space through convolution and pooling after aggregation, but in the separate case, this space does not exist, thus reducing the mining of a portion of the information. \(M_{shift-cross}\) contains the interaction space of \(M_{shift}\) and \(M_{cross}\) parts, which can be extracted under the convolutional pooling;
4. There is an overlap between the adjacent granularities, allowing for the partial substitution between the granularities so that the information extracted in the non-shifted case can still be overlapped under the shifted matching. These conjectures give us new anticipation as to whether there is a better way to achieve lower computational effort and higher accuracy with less matching matrix that can cover a more comprehensive range of information.