We propose the first unsupervised approach to the problem of modeling dialogue acts in an open domain. Trained on a corpus of noisy Twitter conversations, our method discovers dialogue acts by clustering raw utterances. Because it accounts for the sequential behaviour of these acts, the learned model can provide insight into the shape of communication in a new medium. We address the challenge of evaluating the emergent model with a qualitative visualization and an intrinsic conversation ordering task. This work is inspired by a corpus of 1.3 million Twitter conversations, which will be made publicly available.
REF considers the task of discovering dialog acts by clustering the raw utterances.
Unsupervised Modeling of Twitter Conversations
Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Computer Science