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Explore ChatGPT in Affective Computing

Introduction

We want to test the capacity of ChatGPT on Affective Computing. This repository contains the result of Dialogue Act Classification(DAC) for now and we will update more results in few weeks.

Result for DAC

Model Acc weighted-F1
Co-GAT - 79.4
ChatGPT, oneshot 0.67 0.65
ChatGPT, oneshot+prompt-engineering 0.71 0.70
ChatGPT, fewshot 0.73 0.72
ChatGPT, fewshot+prompt-engineering 0.74 0.73

Project Structure

  • cal_prf.py

    input: two label files which are chatGPT prediction and golden answer file

    output: the classification report using sklearn

  • check_utts

​ input: output file of chatGPT and golden answer file

​ output: the indexes of dialogue which are obviously wrongly annotated.

  • dd_label_extract.py: extract the labels from output file of chatGPT into a label file.

  • dd-dialogue-act.py: sample 64 dialogs in daily dialogue and transform the data format.

  • main.py: use ReverseChatGPT API to use chatGPT