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The pre-trained conversational models still fail to capture the implicitcommonsense (CS) knowledge hidden in the dialogue interaction, even though theywere pre-trained with an enormous dataset. In order to build a dialogue agentwith CS capability, we firstly inject external knowledge into a pre-trainedconversational model to establish basic commonsense through efficient Adaptertuning (Section 4). Secondly, we propose the ``two-way learning'' method toenable the bidirectional relationship between CS knowledge and sentence pairsso that the model can generate a sentence given the CS triplets, also generatethe underlying CS knowledge given a sentence (Section 5). Finally, we leveragethis integrated CS capability to improve open-domain dialogue responsegeneration so that the dialogue agent is capable of understanding the CSknowledge hidden in dialogue history on top of inferring related otherknowledge to further guide response generation (Section 6). The experimentresults demonstrate that CS\_Adapter fusion helps DialoGPT to be able togenerate series of CS knowledge. And the DialoGPT+CS\_Adapter response modeladapted from CommonGen training can generate underlying CS triplets that fitsbetter to dialogue context.

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