Bootstrapping an Unsupervised Approach for Classifying Agreement and Disagreement

ABSTRACT People tend to have various opinions about topics. In discussions, they can either agree or disagree with another person. The recognition of agreement and disagreement is a useful prerequisite for many applications. It could be used by political scientists to measure how controversial political issues are, or help a company to analyze how well people like their new products. In this work, we develop an approach for recognizing agreement and disagreement. However, this is a challenging task. While keyword-based approaches are only able to cover a limited set of phrases, machine learning approaches require a large amount of training data. We therefore combine advantages of both methods by using a bootstrapping approach. With our completely unsupervised technique, we achieve an accuracy of 72.85%. Besides, we investigate the limitations of a keyword based approach and a machine learning approach in addition to comparing various sets of features

A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version

During the past decade, several areas of speech and language understanding have witnessed substantial breakthroughs from the use of data-driven models. In the area of dialogue systems, the trend is less obvious, and most practical systems are still built through significant engineering and expert knowledge. Nevertheless, several recent results suggest that data-driven approaches are feasible and quite promising. To facilitate research in this area, we have carried out a wide survey of publicly available datasets suitable for data-driven learning of dialogue systems. We discuss important characteristics of these datasets, how they can be used to learn diverse dialogue strategies, and their other potential uses. We also examine methods for transfer learning between datasets and the use of external knowledge. Finally, we discuss appropriate choice of evaluation metrics for the learning objective