Consistent Response Generation with Controlled Specificity

Abstract

We propose a method to control the specificity of responses while maintaining the consistency with the utterances for open-domain conversation systems. We first design a metric based on pointwise mutual information, which measures the co-occurrence degree between an utterance and a response. To control the specificity of the generated responses, we add the distant supervision based on the cooccurrence degree and a PMI-based word prediction mechanism to a sequence-to-sequence model. Using these mechanisms, our model outputs the words with desired specificity for a given specificity level. In experiments with open-domain dialogue corpora, automatic and human evaluation results confirm that our model controls the specificity of the responses more sensitively than the conventional model and can generate highly consistent responses.

SideControl: Controlled Open-domain Dialogue Generation via Additive Side Networks

“…model on specific datasets with task-specific design on model architecture (Wen et al, 2015;Ke et al, 2018;Chen et al, 2019;See et al, 2019) or policy learning strategy (Kawano et al, 2019;Hsueh and Ma, 2020;Takayama and Arase, 2020;Varshney et al, 2021). In this work, we explore effective method for controlled generation on Transformerbased dialogue systems, with the goal of adding controllability functionality into state-of-the-art Transformer-based dialogue systems with lower computation cost, less training data and more flexible control mechanism.…”