COMMA : Modeling Relationship among Motivations, Emotions and Actions in Language-based Human Activities (2022)

Abstract: Motivations, emotions, and actions are inter-related essential factors in human activities. While motivations and emotions have long been considered at the core of exploring how people take actions in human activities, there has been relatively little research supporting analyzing the relationship between human mental states and actions. We present the first study that investigates the viability of modeling motivations, emotions, and actions in language-based human activities, named COMMA (Cognitive Framework of Human Activities). Guided by COMMA, we define three natural language processing tasks (emotion understanding, motivation understanding and conditioned action generation), and build a challenging dataset Hail through automatically extracting samples from Story Commonsense. Experimental results on NLP applications prove the effectiveness of modeling the relationship. Furthermore, our models inspired by COMMA can better reveal the essential relationship among motivations, emotions and actions than existing methods.

## introduction human activities are continuous interactions between external environment (physical world and social events, etc.) and internal mind (motivations, emotions, etc.). for example, figure 1 demonstrates human activities of character 'i' about 'eating bread' in external environment, as well as the mental states of 'i'. the motivation of character 'i' is a physiological need. conditioned on this motivation and history actions, 'i' enjoyed the part of bread on the direction of joy emotion. while human mental states have long been considered at the core of exploring how people take actions between the lines in language-based human activities, there has been relatively little research supporting analyzing the relationship between human mental states † corresponding author. ‡ we will make our dataset and code publicly available at https://github.com/indexfziq/comma . motivation i have a physiological need. however, i enjoyed the part that i took out. ## comma human activities are interactions between internal mind of people and the external environment. in this part, we will describe the basic elements of comma and modeling the relationship among elements in details. ## tasks and data to verify the effectiveness of comma, we propose emotion understanding, motivation understanding, and conditioned action generation tasks. correspondingly, we build a hail dataset by automatically extracting samples with complete mental state annotation from story commonsense @xcite . ## conclusion and future work in this paper, we propose a cognitive framework of human activities (comma). to verify the effectiveness of our cognitive framework, we introduce three challenging nlp tasks, automatically construct a dataset hail, and propose the corresponding methods. experimental results show a better understanding of the relationship among motivations, emotions and actions under our comma than existing methods. modeling the relationship among motivations, emotions and actions in human activities can allow researchers to reason the essential causes of human activities from the cognitive perspective and supply reasonable explanations. in future work, we will explore comma on various nlp downstream applications, such as intelligent dialogue, controllable text generation and public opinion analysis. besides, it is interesting to further analyse more complex human activities. cog-gpt-2: tom was eating a big bowl of cereal. human: he book a trip to the yukon to fulfill his dream. human: marc offers to buy tom skyline chilli if he pays him back. means that the generated action can not express the corresponding aspect. represents that the consistency is debatable. the colored text means generated action satisfy the aspects of mental states. searchers to reason the essential causes of human activities from the cognitive perspective and supply reasonable explanations. in future work, we will explore comma on various nlp downstream applications, such as intelligent dialogue, controllable text generation and public opinion analysis. besides, it is interesting to further analyse more complex human activities.