This paper describes the HIT-SCIR emotional response agent "Babbling" to the NLPCC 2017 Shared Task 4 on emotional conversation generation. Babbling consists of two parts, one is a rule based model for picking generic responses and the other is a neural work based model. For the latter part, we apply the encoder-decoder [1] framework to generate emotional response given the post and assigned emotion label. To improve the content coherency, we use LTS [2] for acquiring a better first word. To generate responses with consistent emotions, we employ the emotion embeddings to guide emotionalizing process. To produce more content coherent and emotion consistent responses, we include the attention mechanism [3] and its extension, multi-hop attention (MTA) [4]. The rule based part and neural network based part are ranked the second and fifth place respectively according to the total score.
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