Papers › A Diversity-Promoting Objective Function for Neural Conversation Models

A Diversity-Promoting Objective Function for Neural Conversation Models

11 Oct 2015NAACL 2016 6arXiv:1510.03055archive 2025-07-28

Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, Bill Dolan

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.

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