Data-oriented Approach for Lookahead Response Generation

Abstract

In human-human conversation, the current utterance in a dialog is often influenced by previous and future contexts. Among these, looking ahead over future context is one of the most critical factors for active conversation. In this paper, we propose a novel training strategy to help neural response generation models generate responses that take into account information from the future context. Our training strategy considers a sequence consisting of the response and its future context as an output sequence, and the model learns to generate the output sequence from an input sequence, i.e., past utterances. In our experiments, we investigate the effect of the proposed strategy on the look-ahead ability of a dialog system via the "Lookahead Chit Chat Task."