Answer Identification in Collaborative Organizational Group Chat

TL;DR

This paper introduces an unsupervised Kernel Density Estimation-based clustering method called Ans-Chat for identifying answers in organizational group chats, addressing challenges posed by intertwined conversations and structural differences.

Contribution

The paper presents a novel unsupervised approach that adapts to different chat structures without requiring channel-specific tagging, improving answer identification accuracy.

Findings

Outperforms existing methods in answer identification accuracy

Effectively handles structural and lexical variability in chat groups

Eliminates need for manual tagging or channel-specific models

Abstract

We present a simple unsupervised approach for answer identification in organizational group chat. In recent years, organizational group chat is on the rise enabling asynchronous text-based collaboration between co-workers in different locations and time zones. Finding answers to questions is often critical for work efficiency. However, group chat is characterized by intertwined conversations and 'always on' availability, making it hard for users to pinpoint answers to questions they care about in real-time or search for answers in retrospective. In addition, structural and lexical characteristics differ between chat groups, making it hard to find a 'one model fits all' approach. Our Kernel Density Estimation (KDE) based clustering approach termed Ans-Chat implicitly learns discussion patterns as a means for answer identification, thus eliminating the need to channel-specific tagging. Empirical evaluation shows that this solution outperforms other approached.