A Socratic epistemology for verbal emotional intelligence

We describe and experimentally validate a question-asking framework for machinelearned linguistic knowledge about human emotions. Using the Socratic method as a theoretical inspiration, we develop an experimental method and computational model for computers to learn subjective information about emotions by playing emotion twenty questions (EMO20Q), a game of twenty questions limited to words denoting emotions. Using human–human EMO20Q data we bootstrap a sequential Bayesian model that drives a generalized pushdown automaton-based dialog agent that further learns from 300 human–computer dialogs collected on Amazon Mechanical Turk. The human–human EMO20Q dialogs show the capability of humans to use a large, rich, subjective vocabulary of emotion words. Training on successive batches of human–computer EMO20Q dialogs shows that the automated agent is able to learn from subsequent human–computer interactions. Our results show that the training procedure enables the agent to learn...

• Type: Journal
• Year: 2016
• Where: PEERJCS
• Authors: Abe Kazemzadeh, James Gibson, Panayiotis G. Georgiou, Sungbok Lee, Shrikanth Narayanan
• Keywords: PEERJCS 2016