# Knowledge Interpolated Conditional Variational Auto-Encoder for Knowledge Grounded Dialogues
## 6 Experiments and Analysis
### Metric-Based Evaluation
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Test Seen** & \multicolumn{8}{c}{**Gold Knowledge**} & \multicolumn{8}{c}{**Predicted Knowledge**} \\ \hline
**Method** & **PPL** & **AVE** & **EXT** & **GRY** & **Dist\({}_{1}\)** & **Dist\({}_{2}\)** & \(\Delta\text{B}_{k}\) & **PPL** & **AVE** & **EXT** & **GRY** & **Dist\({}_{1}\)** & **Dist\({}_{2}\)** & \(\Delta\text{B}_{k}\) \\ \hline HRED & 25.1 & 88.6 & 53.7 & 83.9 & 3.7 & 22.3 & 9.3 & 36.5 & 88.1 & 53.6 & 84.9 & 3.5 & 21.6 & 11.1 \\ CVAE & 27.6 & 87.0 & 51.2 & 82.1 & 5.2 & 30.2 & 9.2 & 42.1 & 87.4 & 52.1 & 83.6 & 5.4 & 31.7 & 8.0 \\ GTTP & 20.5 & 89.9 & 54.5 & 85.2 & 4.8 & 26.3 & 11.4 & 38.8 & 88.9 & 53.2 & 84.2 & 4.2 & 25.1 & 10.7 \\ TMem & 23.1 & 87.4 & 52.9 & 83.4 & 4.1 & 23.9 & 10.9 & 40.5 & 88.2 & 53.7 & 84.6 & 3.5 & 26.6 & 11.7 \\ SKT & 53.3 & 85.5 & **90.1** & 64.8 & **8.4** & 30.0 & **2.17** & 59.2 & 86.3 & **88.2** & 66.3 & **7.8** & 29.1 & **2.21** \\ \hline KIV\({}_{\text{c}}\) & 20.8 & 91.5 & 54.7 & 84.3 & 6.2 & 32.8 & 10.4 & 29.7 & 90.2 & 53.9 & 83.3 & 5.3 & 31.7 & 12.0 \\ KIV\({}_{\text{d}}\) & **15.6** & **92.8** & 56.4 & 85.8 & 7.1 & 33.7 & 3.5 & **25.3** & **90.4** & 55.3 & **86.6** & 6.5 & 34.1 & 4.5 \\ KIV\({}_{\text{p}}\) & 19.3 & 91.6 & 55.8 & **86.9** & **8.4** & **37.2** & 2.8 & 31.5 & 89.9 & 54.0 & 86.4 & 7.6 & **35.8** & 2.7 \\ \hline \hline
**Test Unseen** & \multicolumn{8}{c}{**Gold Knowledge**} & \multicolumn{8}{c}{**Predicted Knowledge**} \\ \hline
**Method** & **PPL** & **AVE** & **EXT** & **GRY** & **Dist\({}_{1}\)** & **Dist\({}_{2}\)** & \(\Delta\text{B}_{k}\) & **PPL** & **AVE** & **EXT** & **GRY** & **Dist\({}_{1}\)** & **Dist\({}_{2}\)** & \(\Delta\text{B}_{k}\) \\ \hline HRED & 56.2 & 88.3 & 52.5 & 84.0 & 2.4 & 19.4 & 8.9 & 88.5 & 87.2 & 51.4 & 82.0 & 2.2 & 18.9 & 11.8 \\ CVAE & 68.7 & 85.2 & 50.5 & 81.9 & 3.0 & 24.9 & 10.3 & 75.3 & 84.6 & 50.3 & 81.8 & 2.8 & 22.6 & 9.2 \\ GTTP & 49.1 & 86.9 & 51.8 & 83.8 & 2.8 & 26.7 & 13.0 & 81.9 & 85.9 & 52.3 & 83.5 & 2.5 & 22.4 & 11.0 \\ TMem & 47.3 & 86.3 & 52.2 & 81.5 & 2.6 & 21.2 & 9.8 & 70.6 & 87.1 & 52.9 & 82.4 & 2.5 & 20.4 & 10.9 \\ SKT & 83.0 & 84.5 & **89.3** & 62.8 & **5.76** & 19.3 & **2.74** & 92.1 & 82.5 & **87.6** & 63.5 & **5.21** & 17.3 & **3.16** \\ \hline KIV\({}_{\text{c}}\) & 49.6 & 89.1 & 52.7 & 84.3 & 2.8 & 22.6 & 9.6 & 65.4 & 88.7 & 52.5 & 83.9 & 2.7 & 21.8 & 9.2 \\ KIV\({}_{\text{d}}\) & 44.2 & **90.2** & 53.6 & **84.5** & 3.5 & 23.8 & 4.2 & 57.2 & 89.3 & 52.8 & **83.7** & 3.3 & 24.6 & 4.7 \\ KIV\({}_{\text{p}}\) & **38.7** & 89.7 & 55.9 & 82.8 & 4.8 & **30.2** & 3.1 & **50.9** & **90.2** & 53.6 & 82.4 & 4.0 & **29.5** & 3.5 \\ \hline \hline \end{tabular}  
The numbers in **Bold** represent the best results for the corresponding measure.  
\end{table} 
Table 1: Automatic evaluation results on WoW Dataset for Test Seen and Test Unseen setting. \(\Delta\text{B}_{k}\) in table denotes \(\Delta\text{BLEU}_{k}\). that interpolating two latent variables, conditioned on knowledge and context, aids in generating more appropriate and informative responses. A comparison between interpolation fusion methods (KIVd,p) and non-interpolation fusion (KIVc) reveals that interpolation significantly enhances the quality of responses. This indicates that interpolation is more appropriate for fusing knowledge and context in knowledge grounded conversations. Our analysis of knowledge engagement metric ABLEUk shows that KIVp achieves the lowest score, except that it is slightly higher than SKT. This indicates that interpolating latent variables using probabilistic weights can adaptively learn the joint representation of context and knowledge, leading to the generation of more engaging responses with respect to the incorporated knowledge. Similar results were also observed on Holl-E in Table 2. The Dist1,2 in KIVp is lower than KIVc,d, suggesting that KIVp use more copied knowledge, while KIVc,d generate a greater range of content that may not be from the knowledge base.