Answer Identification in Collaborative Organizational Group Chat
This addresses efficiency challenges for workers using asynchronous group chats, though it appears incremental as it builds on existing clustering techniques.
The paper tackles the problem of identifying answers to questions in organizational group chats, where intertwined conversations and varying characteristics make it difficult to pinpoint answers. Their unsupervised Kernel Density Estimation-based clustering approach outperforms other methods in empirical evaluation.
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.