## 5 Experiments
### Response Generator  
The performance of RGRW is compared with those of five baselines, which are TS2S, COPY, GenDS, CCM and ConKADI. TS2S is a transformer with six blocks of an encoder and a decoder. COPY is an LSTM-based sequence-to-sequence model with the copy mechanism [37] and GenDS generates responses from the candidate facts retrieved from a knowledge base [38]. CCM [13] and ConKADI [6] are commonsense knowledge-aware response generators. The responses in the training set are used as posterior knowledge when training ConKADI, but they are not used in CCM. The performance of the models is measured with eight automatic metrics. Emb\({}_{avg}\)[39] is the similarity between the average embedding vector of a ground-truth response and that of the generated response, while Emb\({}_{cx}\) measures the similarity between embedding vectors using vector extrema. Bleu-2 and Bleu-3 are the ratio of bi-gram and tri-gram overlaps, respectively [40], and Dist-1 and Dist-2 are the ratio of distinct uni-grams and bi-grams in all generated responses [7]. Entropy is the average word-level entropy [41]. The \(R_{d}\) is the relative score of a model when the arithmetic mean of metric scores of all other comparison models is set to 1.00 [6]. The results on these metrics are shown at Table 4. RGRW achieves the highest scores for all metrics, except Bleu and Emb\({}_{avg}\). For both Bleu-2 and Bleu-3, RGRW shows the second lowest performance. The generated responses are often acceptable, even if they are different from the ground-truth responses. Thus, the sole measurement with Bleu has a limit to evaluate the quality of responses. The embedding metric is one of the metrics that solve the limitation of Bleu. It compares the contexts of the generated responses and the ground-truth responses. GenDS shows the highest performance in Emb\({}_{avg}\), while RGRW achieves the best performance in Emb\({}_{cx}\). In general, the embedding vectors of general words are located close to the origin and the vectors of contextually important words are far from the origin. Thus, Emb\({}_{cx}\) focuses more on contextually important words than general words [39]. In other words, the responses generated by RGRW are more similar to the key topics of ground-truth responses than those by other baselines. Dist-1, Dist-2, and entropy measure how diverse and informative the generated responses are. RGRW shows the best performance for these metrics. One thing to note is that the higher the Bleu score that a model shows, the lower the diversity and informative scores that it achieves. This is because focusing on lexical coincidence with the ground-truth response affects the generation of diverse responses negatively. Finally, RGRW outperforms all baselines in \(R_{d}\). In particular, it shows 0.07 higher \(R_{d}\) than ConKADI, the best baseline. Summarizing all the results, RGRW generates a response that is not only lexically similar, but also contextually similar to a ground-truth response. In addition, RGRW generates more diverse and informative responses by adopting the response weight. All these results prove that the response generation through response weight is effective in producing diverse and context-preserving responses.