# Picking Pearl From Seabed: Extracting Artefacts from Noisy Issue Triaging Collaborative Conversations for Hybrid Cloud Services

arXiv: https://arxiv.org/abs/2105.15065 · HTML: https://mlsanity.com/paper/2105.15065/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 15 references that are in the map.
- First posted on arXiv 31 May 2021.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [BERT for Joint Intent Classification and Slot Filling](https://mlsanity.com/paper/1902.10909/) (2019)
- [The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://mlsanity.com/paper/1506.08909/) (2015)
- [Dialogue Act Sequence Labeling using Hierarchical encoder with CRF](https://mlsanity.com/paper/1709.04250/) (2017)

## Built on by (most cited first, 6 of 6)

- [Recommending Root-Cause and Mitigation Steps for Cloud Incidents using Large Language Models](https://mlsanity.com/paper/2301.03797/) (2023)
- [Automated Root Causing of Cloud Incidents using In-Context Learning with GPT-4](https://mlsanity.com/paper/2401.13810/) (2024)
- [X-lifecycle Learning for Cloud Incident Management using LLMs](https://mlsanity.com/paper/2404.03662/) (2024)
- [Simplifying Root Cause Analysis in Kubernetes with StateGraph and LLM](https://mlsanity.com/paper/2506.02490/) (2025)
- [PACE-LM: Prompting and Augmentation for Calibrated Confidence Estimation with GPT-4 in Cloud Incident Root Cause Analysis](https://mlsanity.com/paper/2309.05833/) (2023)
- [Dependency Aware Incident Linking in Large Cloud Systems](https://mlsanity.com/paper/2403.18639/) (2024)

## Abstract (arXiv)

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.