With the availability of large datasets and the recent progress made by neural methods, variants of sequence to sequence learning (seq2seq) (Sutskever et al., 2014) architectures have been successfully applied for building conversational systems (Serban et al., , 2017b.
despite these methods being the stateof-the art frameworks for conversation generation, they suffer from problems such as lack of diversity in responses and generation of short, repetitive and uninteresting responses (Liu et al., 2016;Serban et al., , 2017b.