Exploring the Role of Context in Utterance-level Emotion, Act and Intent Classification in Conversations: An Empirical Study
Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, Soujanya Poria
Abstract
The recent abundance of conversational data on the Web and elsewhere calls for effective NLP systems for dialogue understanding. Complete utterance-level understanding often requires context understanding, partly defined by the nearby utterances and by the user intention and background. In recent years, a number of context-aware approaches have been proposed for various utterance-level dialogue understanding tasks. In this paper, we explore and quantify the role of context for different aspects of a dialogue, namely emotion, dialogue act, and intent identification, using stateof-the-art dialogue understanding methods as baselines. Specifically, we employ various perturbations to distort the context of a given utterance and study its impact on the different tasks and baselines. This provides us with insights into the fundamental context factors that have immediate implications on different aspects of a dialogue. Such insights may inspire more effective dialogue understanding models and provide support for future text generation approaches.