  The recent abundance of conversational data on the Web and elsewhere calls
for effective NLP systems for dialog understanding. Complete utterance-level
understanding often requires context understanding, defined by nearby
utterances. In recent years, a number of approaches have been proposed for
various utterance-level dialogue understanding tasks. Most of these approaches
account for the context for effective understanding. In this paper, we explore
and quantify the role of context for different aspects of a dialogue, namely
emotion, intent, and dialogue act identification, using state-of-the-art dialog
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 contextual controlling factors of different aspects of a dialogue.
Such insights can inspire more effective dialogue understanding models, and
provide support for future text generation approaches. The implementation
pertaining to this work is available at
https://github.com/declare-lab/dialogue-understanding.
