Utterance-level Dialogue Understanding: An Empirical Study
This work addresses the need for effective NLP systems in dialogue understanding by providing empirical insights that could inspire better models, but it is incremental as it builds on existing methods.
The study quantifies the role of context in utterance-level dialogue understanding tasks like emotion, intent, and dialogue act identification by perturbing context and analyzing its impact on state-of-the-art baselines, finding insights into contextual controlling factors.
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.