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Sharing ideas through communication with peers is the primary mode of humaninteraction. Consequently, extensive research has been conducted in the area ofconversational AI, leading to an increase in the availability and diversity ofconversational tasks, datasets, and methods. However, with numerous tasks beingexplored simultaneously, the current landscape of conversational AI becomesfragmented. Therefore, initiating a well-thought-out model for a dialogue agentcan pose significant challenges for a practitioner. Towards highlighting thecritical ingredients needed for a practitioner to design a dialogue agent fromscratch, the current study provides a comprehensive overview of the primarycharacteristics of a dialogue agent, the supporting tasks, their correspondingopen-domain datasets, and the methods used to benchmark these datasets. Weobserve that different methods have been used to tackle distinct dialoguetasks. However, building separate models for each task is costly and does notleverage the correlation among the several tasks of a dialogue agent. As aresult, recent trends suggest a shift towards building unified foundationmodels. To this end, we propose UNIT, a UNified dIalogue dataseT constructedfrom conversations of existing datasets for different dialogue tasks capturingthe nuances for each of them. We also examine the evaluation strategies used tomeasure the performance of dialogue agents and highlight the scope for futureresearch in the area of conversational AI.

Dialogue Agents 101: A Beginner's Guide to Critical Ingredients for Designing Effective Conversational Systems