ConceptNet infused DialoGPT for Underlying Commonsense Understanding and Reasoning in Dialogue Response Generation

Abstract

The pre-trained conversational models still fail to capture the implicit commonsense (CS) knowledge hidden in the dialogue interaction, even though they were pre-trained with an enormous dataset. In order to build a dialogue agent with CS capability, we firstly inject external knowledge into a pre-trained conversational model to establish basic commonsense through efficient Adapter tuning (Section 4). Secondly, we propose the ``two-way learning'' method to enable the bidirectional relationship between CS knowledge and sentence pairs so that the model can generate a sentence given the CS triplets, also generate the underlying CS knowledge given a sentence (Section 5). Finally, we leverage this integrated CS capability to improve open-domain dialogue response generation so that the dialogue agent is capable of understanding the CS knowledge hidden in dialogue history on top of inferring related other knowledge to further guide response generation (Section 6). The experiment results demonstrate that CS\_Adapter fusion helps DialoGPT to be able to generate series of CS knowledge. And the DialoGPT+CS\_Adapter response model adapted from CommonGen training can generate underlying CS triplets that fits better to dialogue context.