# Research on Applying the \"Shift\" Concept to Deep Attention Matching
## 5 Conclusions  
This paper proposes two improved methods based on DAM with the idea of \"shift\". The first improvement is to calculate the similarity of the shifted granularity in the matching stage and construct a new matching matrix to load the mapping information between the shifted granularities as a supplement to the original model. The second improvement, the multilevel shifted cross-attention granularity matching, is based on the shortcomings of the previously proposed method. To address the shortcomings of the shifted granularity matching method, it is still guided by the idea of \"shift\", allowing for a deeper dependency \begin{table}
\begin{tabular}{l 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 Ours-1 & 0.941 & 0.776 & 0.881 & 0.970 & 0.558 & 0.604 & 0.431 & 0.257 & 0.425 & 0.769 \\ \hline Only \(M_{shift}\) & 0.901 & 0.722 & 0.830 & 0.922 & 0.511 & 0.556 & 0.359 & 0.212 & 0.397 & 0.721 \\ \hline Only \(M_{self}\) & 0.931 & 0.741 & 0.859 & 0.964 & 0.527 & 0.574 & 0.382 & 0.221 & 0.403 & 0.750 \\ \hline Only \(M_{cross}\) & 0.932 & 0.749 & 0.863 & 0.966 & 0.535 & 0.585 & 0.400 & 0.234 & 0.411 & 0.733 \\ \hline Only \(M_{shift-cross}\) & 0.935 & 0.761 & 0.869 & 0.966 & 0.546 & 0.592 & 0.418 & 0.245 & 0.408 & 0.751 \\ \hline DAM & 0.938 & 0.767 & 0.874 & 0.969 & 0.550 & 0.601 & 0.427 & 0.254 & 0.410 & 0.757 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: The results of the ablation experiment for the two proposed methods. mapping between the shifted granularities, with the improvement in the \"representation\" phase. Inspired by the decoder structure in the transformer, where the shifted response and utterance granularities are conducted the cross attention to obtain new shifted granularities, which is then used to construct a new matching matrix that complements the information. We have demonstrated the effectiveness of the two methods by comparative experiments. What we need to do in the future can be summarized as follows: (1) The way to realize the idea of \"dislocation\" matching in this paper is at the cost of linearly increasing the calculation amount and memory of model training. To solve this problem, we need to consider how to ensure that the information in the \"dislocation\" can be mined with less computation and memory overhead. This will involve how to more effectively represent the overall mapping relationship between the response and utterance, and compress more information into a less granular representation; Moreover, the two methods proposed in this paper are to add a new matching matrix. Although the matrix has new information to be mined, it is not difficult to conclude from the ablation experiments that the matrix contains most of the existing information of the original matrix. How to reduce the extraction of redundant information is also the focus of future research. (2) The other problem that needs to be studied in the future is how to verify the mechanical principle of \"dislocation\". Although better results can be obtained through dislocation, the interaction principle between the dislocation granularities has not been proved through experimental logic. What needs to be carried out in the future is to further study the principle of interaction between the different granularities, and prove the rationality of the \"dislocation\" idea proposed in this paper through visual results. At the same time, it can also be verified by the experiments that this dislocation matching method can be used not only in the depth attention matching model but also in other models with a similar point-to-point matching structure. Conceptualization, methodology, software, validation, formal analysis, investigation, resources, data curation, writing--original draft preparation, K.H.