One research focus of human-centered teamwork is on advanced decision architectures that can help people make effective and timely decisions. This requires distributed team members to effectively establish shared situation awareness and to collaboratively develop explanations on how an unfamiliar situation might have been emerging. One key to achieve this goal is the ability to anticipate others' future information needs and to offer help proactively. In this paper we investigate a novel approach to anticipating teammates' information needs based on step-wise conversation pattern recognition, leveraging the idea of multi-party communication. This approach can be further extended to build a computational model for collaborative story building as needed in recognition-primed, naturalistic decision architectures.