EmoInHindi: A Multi-label Emotion and Intensity Annotated Dataset in Hindi for Emotion Recognition in Dialogues

Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022), pages 5829–5837 Marseille, 20-25 June 2022 © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0 5829 EmoInHindi: A Multi-label Emotion and Intensity Annotated Dataset in Hindi for Emotion Recognition in Dialogues Gopendra Vikram Singh*,Priyanshu Priya*,Mauajama Firdaus*,Asif Ekbal,Pushpak Bhattacharyya Department of Computer Science and Engineering Indian Institute of Technology Patna, Patna, India {gopendra_1921cs15, priyanshu_2021cs26, mauajama.pcs16, asif, pb}@iitp.ac.in Abstract The long-standing goal of Artificial Intelligence (AI) has been to create human-like conversational systems. Such systems should have the ability to develop an emotional connection with the users, hence emotion recognition in dialogues is an important task. Emotion detection in dialogues is a challenging task because humans usually convey multiple emotions with varying degrees of intensities in a single utterance. Moreover, emotion in an utterance of a dialogue may be dependent on previous utterances making the task more complex. Emotion recognition has always been in great demand. However, most of the existing datasets for multi-label emotion and intensity detection in conversations are in English. To this end, we create a large conversational dataset in Hindi named EmoInHindi for multi-label emotion and intensity recognition in conversations containing 1,814 dialogues with a total of 44,247 utterances. We prepare our dataset in a Wizard-of-Oz manner for mental health and legal counselling of crime victims. Each utterance of the dialogue is annotated with one or more emotion categories from the 16 emotion classes including neutral, and their corresponding intensity values. We further propose strong contextual baselines that can detect emotion(s) and the corresponding intensity of an utterance given the conversational context. Keywords: Multi-label Emotion and Intensity Recognition, Dialogues, Low-resource Language 1. Introduction Emotions are fundamental human characteristics that have been researched for many years by re- searchers in psychology, sociology, medicine, com- puter science, and other domains. Ekman’s six- class categorization (Ekman, 1992) and Plutchik’s Wheel of Emotion which proposed eight basic bipo- lar emotions (Plutchik and Kellerman, 2013), are two notable works in understanding and categoris- ing human emotions. Emotions play an impor- tant role in our daily life and emotion detection in text has become a longstanding goal in Natural Language Processing (NLP). Emotions are inher- ently conveyed by messages in human communica- tions. With the popularity of social media plat- forms like Facebook Messenger, WhatsApp and conversational agents like Amazon’s Alexa, there is a growing demand for machines to interpret hu- man emotions in real conversations for more per- sonalized and human-like interactions. The ability to effectively identify emotions in con- versations is crucial for developing robust dialogue systems. There are two major types of dialogue systems: a task-oriented dialogue systems and a social (chit-chat) dialogue system. The former is concerned with creating a personal assistant ca- pable of performing specific tasks, but the latter is concerned with capturing the conversation flow, which focuses more on the speaker’s feelings. In both these systems, understanding the user’s emo- * The authors are jointly the first authors tions is crucial for providing better user …

Content truncated to 500 words