Exploring the Role of Context in Utterance-level Emotion, Act and Intent Classification in Conversations: An Empirical Study

Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pages 1435–1449 August 1–6, 2021. ©2021 Association for Computational Linguistics 1435 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† † Singapore University of Technology and Design, Singapore △ University of Michigan, USA deepanway ghosal@mymail.sutd.edu.sg {navonil majumder, sporia}@sutd.edu.sg mihalcea@umich.edu I think I mentioned once before that I’ve only had three minute liquor glasses of brandy this whole evening. Can you pass my tickets? Intent: find ticket Emotion: angry Oh shut up! I do not remember of any tickets. Emotion: angry The one that I bought last week and you kept them. Get me those now. Intent: find ticket Emotion: frustrated Not very funny, dear. You’d better have some more brandy. Emotion: angry coreferential information Very good idea. I will. Emotion: angry Tone: sarcastic inter-speaker dependency label dependency inter-speaker dependency Can I get your phone number, please? Intent: find phone number Emotion: neutral Uh. Yeah, I just gave it to the automated thing like five times. Emotion: frustrated I am going to need it again. I need to look at your file. Please calm down Sir. I am here to help you. Intent: find phone number Emotion: frustrated Okay. 3236975066. Thank you! Emotion: neutral coreferential information Thank you! Let me check it right way. Emotion: neutral inter-speaker dependency label dependency inter-speaker dependency coreferential information inter-speaker dependency label shift due to other speaker’s response Figure 1: Role of Context in Utterance Level Dialogue Understanding. Abstract The recent abundance of conversational data on the Web and elsewhere calls for effec- tive NLP systems for dialogue understanding. Complete utterance-level understanding often requires context understanding, partly defined by the nearby utterances and by the user inten- tion and background. In recent years, a num- ber 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, dia- logue act, and intent identification, using state- of-the-art dialogue understanding methods as baselines. Specifically, we employ various per- turbations to distort the context of a given utter- ance 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 ap- proaches. 1 Introduction Human-like conversational systems are a long- standing goal of Artificial Intelligence (AI). How- ever, the development of such systems is not a trivial task, as we often participate in dialogues by relying on several contextual factors such as emotions, prior assumptions, intent, or personality traits. It is thus not surprising that the landscape of dialogue understanding research embraces several challenging tasks, such as, emotion recognition in conversations (ERC), dialogue intent classification, user-state representation, and others. These tasks are often performed at utterance level and can …

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