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In this work we leverage commonsense knowledge in form of knowledge paths toestablish connections between sentences, as a form of explicitation of implicitknowledge. Such connections can be direct (singlehop paths) or requireintermediate concepts (multihop paths). To construct such paths we combine twomodel types in a joint framework we call Co-nnect: a relation classifier thatpredicts direct connections between concepts; and a target prediction modelthat generates target or intermediate concepts given a source concept and arelation, which we use to construct multihop paths. Unlike prior work thatrelies exclusively on static knowledge sources, we leverage language modelsfinetuned on knowledge stored in ConceptNet, to dynamically generate knowledgepaths, as explanations of implicit knowledge that connects sentences in texts.As a central contribution we design manual and automatic evaluation settingsfor assessing the quality of the generated paths. We conduct evaluations on twoargumentative datasets and show that a combination of the two model typesgenerates meaningful, high-quality knowledge paths between sentences thatreveal implicit knowledge conveyed in text.

CO-NNECT: A Framework for Revealing Commonsense Knowledge Paths as Explicitations of Implicit Knowledge in Texts