two problems might probably happen when a SEQ2SEQ model is used for generating texts. One is that SEQ2SEQ often ends up with dull and generic responses. Such situation often takes place at conversation systems because safe and meaningless responses such as “I don’t know" or “I’m okay" have frequently appearance and then captured by the decoder. The other is the lack of ability of fitting multiple probable outputs,for the representative vector the decoder used to generate output is fixed and only depends on the inputs. These problems not only reduce the precision, but also limit the diversity of text generation.

To tackle the above problems. Different ways of solutions have been proposed. [@li2016diversity] pointed out that the mutual information of sources and targets should be augmented during the decoding procedure. They proposed the MMI-antiLM algorithm which adds a language model penalty to unconditional high frequent responses. Their algorithm successfully solved the generic and dull response problem, is not applicable to handle the multi-target, since it just consider one target at a time. Beam Search (BS) methods for n-best outputs during the decoding procedure are commonly used in MT and could be introduced to text generation. However, as the greedy strategy 