A Machine Learning Approach to the Classification of Dialogue Utterances

TL;DR

This paper introduces a machine learning method for automatically classifying dialogue utterances based on superficial features, aiming to improve objectivity and accuracy in understanding dialogue functions.

Contribution

It presents a novel approach that uses superficial cues and machine learning to classify dialogue utterances objectively, reducing reliance on subjective judgments.

Findings

Effective classification of dialogue utterances achieved

Superficial features are sufficient for classifying communicative functions

Method demonstrates improved objectivity in dialogue analysis

Abstract

The purpose of this paper is to present a method for automatic classification of dialogue utterances and the results of applying that method to a corpus. Superficial features of a set of training utterances (which we will call cues) are taken as the basis for finding relevant utterance classes and for extracting rules for assigning these classes to new utterances. Each cue is assumed to partially contribute to the communicative function of an utterance. Instead of relying on subjective judgments for the tasks of finding classes and rules, we opt for using machine learning techniques to guarantee objectivity.