  Conversational channels are changing the landscape of hybrid cloud service
management. These channels are becoming important avenues for Site Reliability
Engineers (SREs) %Subject Matter Experts (SME) to collaboratively work together
to resolve an incident or issue. Identifying segmented conversations and
extracting key insights or artefacts from them can help engineers to improve
the efficiency of the incident remediation process by using information
retrieval mechanisms for similar incidents. However, it has been empirically
observed that due to the semi-formal behavior of such conversations (human
language) they are very unique in nature and also contain lot of
domain-specific terms. This makes it difficult to use the standard natural
language processing frameworks directly, which are popularly used in standard
NLP tasks. %It is important to identify the correct keywords and artefacts like
symptoms, issue etc., present in the conversation chats. In this paper, we
build a framework that taps into the conversational channels and uses various
learning methods to (a) understand and extract key artefacts from conversations
like diagnostic steps and resolution actions taken, and (b) present an approach
to identify past conversations about similar issues. Experimental results on
our dataset show the efficacy of our proposed method.
