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Dialog is a core building block of human natural language interactions. Itcontains multi-party utterances used to convey information from one party toanother in a dynamic and evolving manner. The ability to compare dialogs isbeneficial in many real world use cases, such as conversation analytics forcontact center calls and virtual agent design.  We propose a novel adaptation of the edit distance metric to the scenario ofdialog similarity. Our approach takes into account various conversation aspectssuch as utterance semantics, conversation flow, and the participants. Weevaluate this new approach and compare it to existing document similaritymeasures on two publicly available datasets. The results demonstrate that ourmethod outperforms the other approaches in capturing dialog flow, and is betteraligned with the human perception of conversation similarity.

We've had this conversation before: A Novel Approach to Measuring Dialog Similarity