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.
To address this pitfall, REF suggested using Maximum Mutual Information rather than the traditional Maximum-Likelihood Estimation (MLE) objective function, making the responses more interesting.
A Diversity-Promoting Objective Function for Neural Conversation Models
ArXiv
ArXiv
Computer Science