  Site Reliability Engineers (SREs) play a key role in issue identification and
resolution. After an issue is reported, SREs come together in a virtual room
(collaboration platform) to triage the issue. While doing so, they leave behind
a wealth of information which can be used later for triaging similar issues.
However, usability of the conversations offer challenges due to them being i)
noisy and ii) unlabelled. This paper presents a novel approach for issue
artefact extraction from the noisy conversations with minimal labelled data. We
propose a combination of unsupervised and supervised model with minimum human
intervention that leverages domain knowledge to predict artefacts for a small
amount of conversation data and use that for fine-tuning an already pretrained
language model for artefact prediction on a large amount of conversation data.
Experimental results on our dataset show that the proposed ensemble of
unsupervised and supervised model is better than using either one of them
individually.
