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

TL;DR

This paper introduces COMMA, a novel framework for modeling the interrelations of motivations, emotions, and actions in language-based human activities, supported by a new dataset and NLP tasks.

Contribution

The study pioneers the modeling of mental states and actions in language, proposing a framework, defining NLP tasks, and creating a dataset to analyze these relationships.

Findings

Models based on COMMA outperform existing methods in revealing mental state-action relationships.

The dataset Hail enables effective training and evaluation of NLP models for human activity understanding.

Experimental results demonstrate the viability of modeling motivations, emotions, and actions in language-based contexts.

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.