# Research on Applying the \"Shift\" Concept to Deep Attention Matching
## 4 Experiments  
### Dataset  
To facilitate comparison with DAM, we experimented with the same data sets as DAM, Ubuntu dialogue corps, and Douban conversation corps. Ubuntu dialog corps is an English-based data set, which is collected from the chat records of relevant technologies under the Ubuntu system. It contains 500,000 training sets with different contexts. Each context has a positive candidate response and a negative candidate response. The training set also has a combination of 1 million pairs of contexts and responses. As shown in Table 1, the response with Label 1 is the correct candidate response, 0 is the wrong candidate response, and each context corresponds to two candidate responses. Both the verification set and the test set contain 50,000 different contexts, and each context has one positive candidate response and nine negative candidate responses, as shown in Table 2. The positive responses in the data set are all from real human beings' communication, while other negative responses are randomly selected from other data. The data of Douban conversation corps is collected from China's online social platforms and is based on Chinese. The training set contains 1 million context-response pairs, and the verification set has 50,000 pairs. Table 3 illustrates an example. It can be seen that the context discusses topics related to \"watching Friends\" and \"learning English\", while the positive candidate statements do discuss related topics, while the negative candidate statements have no relevance to the context. Each context in the training set and the verification set has a positive candidate response and a negative candidate response. The \begin{table}
\begin{tabular}{l l l} \hline \hline \multicolumn{3}{c}{**Training Set**} \\ \hline
**Contexts** & **Candidate Response** & **Label** \\ \hline hey \(\dots\) i installed a para card in my comp with & & \\ \(\_\)number\(\_\)hd s on it dmesg says the card is & that is already loaded \(\dots\) part & \\ there (it seems to be using the libata module) but & of ide\(\_\)core in lsmod but no & 1 \\ the drives on the card \(\dots\) has someone & \(\_\)path\(\_\) for the disks \(\{\) & \\ experience with these kinds of cards & & \\ \hline maybe the dmesg has some clue can you post it & & \\ to pastebin? & or an option or something its & \\ that’s a bit hard because it’s a different comp & pretty obvious to what it does & 0 \\ with no net but the line that caught my eye was & check out map & \\ ata2 sata link down \(\dots\) it is sata or pata? & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ubuntu Data Set (Training Set). Figure 6: Improved DAM with shifted cross-attention granularity matching method. A new granular representation is generated at the representation part (Blue box), then matched to generate a new matrix (represented in purple), which is finally aggregated with the other two matrices to form a 3D matrix. difference is that the test set has 1000 contexts, each context has 10 candidate response statements, and there are multiple positive candidate responses in these statements. As shown in Table 4.  
The experimental parameters are designed as follows: similar to the deep attention matching model, the experiments use Tensorflow to build a neural network and are carried out on Ubuntu 16 operating system. The hardware platform is a six-core E5-2680 v4 CPU, 30 GB memory, and NVIDIA GEFORCE RTX 3060 graphics card with 12.6 GB GPU memory. Referring to the deep attention matching model, the experiments set the maximum number of chat rounds as 9, and the maximum number of words per utterance is 50. [PERSON]'s word2 vec method is used to preprocess the training set. The size of the FNN parameter is set to 200, which is the same as the size of the embedded word vector. The stack number of self-attention modules is also set to 5.