  Sequence-to-sequence neural network models for generation of conversational
responses tend to generate safe, commonplace responses (e.g., "I don't know")
regardless of the input. We suggest that the traditional objective function,
i.e., the likelihood of output (response) given input (message) is unsuited to
response generation tasks. Instead we propose using Maximum Mutual Information
(MMI) as the objective function in neural models. Experimental results
demonstrate that the proposed MMI models produce more diverse, interesting, and
appropriate responses, yielding substantive gains in BLEU scores on two
conversational datasets and in human evaluations.
