# Research on Applying the \"Shift\" Concept to Deep Attention Matching
## 4 Experiments
### Comparison Methods
In comparison with the other two methods, the shifted granularity matching method also achieves better results for several metrics. In the Ubuntu dataset, the improvement to the sequential matching network on \(R_{2}\)@1 is over 1.62%, on \(R_{10}\)@1 is over 6.89%, on \(R_{10}\)@2 is over 4.01%, in the Douban dataset, on \(MAP\) is over 5.48%, on \(MRR\) is over 6.15%, on \(P\)@1 is over 8.56%, on \(R_{10}\)@1 is over 10.30%, on \(R_{10}\)@2 is over 7.32%, and on \(R_{10}\)@5 is over 6.22%. The reason is that the SMN does not use the attention mechanism to parse the utterance but uses GRU to obtain a granular representation of the subsequence, which is limited to long utterances when encoded in RNNs, and the extraction of interaction information between response and utterance in SMN is executed during convolution and pooling, while DAM uses the attention module to compute cross-attention between the two during the \"representation\" phase. Furthermore, the properties of RNN make it difficult to incrementally parse into multiple layers of granularity, which makes them less informative than DAM. In contrast to the stacked multi-head attention, in the Ubuntu dataset, the improvement to the stacked multi-head attention on \(R_{10}\)@1 is over 0.388%, on \(R_{10}\)@2 is over 0.228%, in the Douban dataset, on \(R_{10}\)@1 is over 1.18%, and on \(R_{10}\)@2 is over 0.711%. The model structure proposed by the stacked multi-head attention is based on the premise that the closer the utterance is to the response the more relevant it is to the response, whereas the shifted granularity matching method is based on the premise that the whole utterances are equal to the response in terms of the model structure, these two different premises, the latter may be better adapted to the current dataset. The \"1 to 3\" in Table 7 indicates the use of the 1 st granularity to the 3 rd granularity for matching under the multilevel shifted cross-attention granularity method, as it gets the best result. In comparison with DAM, in the Ubuntu dataset, the improvement to DAM on \(R_{2}\)@1 is over 0.640%, on \(R_{10}\)@1 is over 1.69%, on \(R_{10}\)@2 is over 1.14%, in the Douban dataset, on \(MAP\) is over 1.82%, on \(P\)@1 is over 2.11%, on \(R_{10}\)@1 is over 2.36%, on \(R_{10}\)@2 is over 2.44%, and on \(R_{10}\)@5 is over 1.72%. In comparison with the shifted granularity matching method, the multi-level shifted cross-attention granularity method achieves better results, in the Ubuntu dataset, the improvement to the shifted granularity matching method on \(R_{2}\)@1 is over 0.425%, on \(R_{10}\)@1 is over 0.515%, on \(R_{10}\)@2 is over 0.341%, in the Douban dataset, on \(MAP\) is over 0.358%, on \(P\)@1 is over 1.16%, and on \(R_{10}\)@1 is over 1.17%. This demonstrates that by implementing shifted matching in the \"representation\" stage upstream of the model, a better shifted matching granularity representation can be resolved to construct a better matching matrix. It demonstrates again the rationality and effectiveness of the processing step of targeting point-to-point mining before extracting information from the representation as a whole. We verify the effectiveness of the two proposed methods through the ablation experiments. Referring to DAM, we select the matching matrix in the matching phase of the model to achieve ablation, for example, only the self-attention matching matrices or the