For utterance representation, we consider the advantages of the two state-of-the-art approaches to encoding contextual information for context-sensitive response generation <cite>(Xing et al., 2017</cite>; Tian et al., 2017) . weighing the importance of utterances for generating open-domain conversational responses<cite> (Xing et al., 2017)</cite> , we thus model the inter-utterance representation to obtain the context vector in two measures, namely static and dynamic attention, as shown in Figure 2 .