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Sequence-to-sequence neural network models for generation of conversationalresponses 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 toresponse generation tasks. Instead we propose using Maximum Mutual Information(MMI) as the objective function in neural models. Experimental resultsdemonstrate that the proposed MMI models produce more diverse, interesting, andappropriate responses, yielding substantive gains in BLEU scores on twoconversational datasets and in human evaluations.

A Diversity-Promoting Objective Function for Neural Conversation Models