## 1 Introduction  
With the advent of sequence-to-sequence models [1, 2], response generation in open domain dialogues has made great progress. Nevertheless, most current response generators often make very general and unattractive responses such as \"I don't know\" or \"What are you talking about?\" [3, 4], since such responses are appropriate to any query. The same response to any query harms the reliability of dialogue-based systems, and thus it is regarded as one of the most critical problems in response generation. Some previous studies noticed that the traditional loss functions, such as maximum likelihood, assign a high probability to _safe_ responses. One solution to avoid safe responses is to inject external knowledge into response generator. [PERSON] et al. adopted unstructured text knowledge [5] and [PERSON] et al. chose structured graph knowledge [6] as external knowledge. However, this approach takes a great amount of memory and searching time during inference because appropriate external knowledge to a given query should be extracted from a knowledge base. Another approach to creating diverse responses is to define a special loss function. The benefit of this approach is that it does not require any external knowledge. [PERSON] et al. showed that the bidirectional influence between a query and its response leads to the generation of more diverse and interesting responses [7]. Thus, they proposed the maximum mutual information as a loss function to model the bidirectional influence. On the other hand, [PERSON] et al. found out that general responses overwhelm specific ones in most dialogue corpora and thus they are preferred by response generators [8]. As a solution to this problem, they proposed the max-marginal ranking loss to highlight the impact of less common but more relevant tokens in a query. However, since these loss functions focus only on the relation among tokens, they fail in capturing the entire context of a response. This paper proposes a novel response generator--Response Generator with Response Weight (RGRW)--which reflects the importance of each token in a query with respect to the whole response. The proposed generator is an encoder-decoder model of which the encoder is pre-trained Bidirectional Encoder Representations from Transformers (BERT) [9] and the decoder is a pre-trained Generative Pre-Training of a language model-2 (GPT-2) [10]. In addition to them, it includes a _response weight_, which captures the importance of every query token with respect to a response [11]. Even though the BERT-encoder is trained to reflect a response, it fails in capturing whole key tokens of a query with respect to the response because it focuses on encompassing the information within a query. Thus, the response weight delivers the relevancy of each query token toward a response for the decoder, that is, it identifies the key query tokens for generating a response. Then, the decoder reflects the response weight in the response generation by paying more attention to the query tokens relevant to the generating response. The effectiveness of the proposed response weight is proved with the short-text keyword detection task [12]. For the experiment, the MAUI twitter data set, which consists of data pairs with short sentences and keywords of sentences, is used. Even if the response weight is not a direct keyword extractor, its performance on the MAUI twitter data set is competitive against those of direct keyword extractors. Especially, it outperforms the keyword extractor trained with non-MAUI twitter data. The performance of the response generation by RGRW is verified with the Reddit data set, an open-domain singleton dialogue data set [13]. According to the experimental results, RGRW shows the highest score among the baselines in Distinct-\(n\) and word-level Entropy, which implies that RGRW generates diverse and informative responses by focusing more on the tokens that are important for generating the response.