Research on conversation has put emphasis on the importance of a multi-level communication system, in which the interlocutors aim to establish and maintain common ground. In natural conversations, repair mechanisms such as clarification requests are frequently used to improve mutual understanding.Here we explore the effects of conversational repair on languages emerging in signaling games. We extend the basic Lewis signaling game setup with a feedback channel that allows for the transmission of messages backwards from the receiver to the sender. Further, we add noise to the communication channel so that repair mechanisms become necessary for optimal performance.We find that for models that were trained with a feedback channel the sender agents produce less compositional messages. However, they still achieve a substantially higher generalization performance, putting to question the role of compositionality for generalization.These findings generalize also to a more realistic case involving naturalistic images in a guessing game setup.More broadly, this study provides an important step towards the creation of signaling games that more closely resemble the conditions under which human languages emerged.