Traditional neural network-based sequence-to-sequence (seq2seq) models strongly suffer from the low diversity problem when it comes to open domain dialogue generation. The authors aim to diversify the dialogue generation with non-conversational text corpus in Chinese language. We attempt to extend this work to conversational and non-conversational datasets in English Analysis on how filtering the non-conversational corpus based on topic affects the result (selected topics: Politics, Attitude & Emotion, Health).