Automatic annotation of dialogues using n-grams

Abstract

The development of a dialogue system for any task implies the acquisition of a dialogue corpus in order to study the structure of the dialogues used in that task. This structure is reflected in the dialogue system behaviour, which can be rule-based or corpus-based. In the case of corpus-based dialogue systems, the behaviour is defined by statistical models which are inferred from an annotated corpus of dialogues. This annotation task is usually difficult and expensive, and therefore, automatic dialogue annotation tools are necessary to reduce the annotation effort. An automatic dialogue labeller technique that is based on n-grams is presented in this work. Its different variants are evaluated with respect to manual human annotations of a dialogue corpus devoted to train queries. © Springer-Verlag Berlin Heidelberg 2006.