Dialogical Emotion Decoding

This is an implementation of Dialogical Emotion Decoder presented in this year ICASSP 2020. In this repo, we use IEMOCAP as an example to evaluate the effectiveness of DED.

Overview

Note

• The performance is better than shown in the paper because I found a little bug in the rescoring part.
• To test on your own emotion classifier, replace data/outputs.pkl with your own outputs.
  • Dict, {utt_id: logit} where utt_id is the utterance name in IEMOCAP.

Requirements

pip3 install virtualenv
virtualenv --python=python3 venv
source venv/bin/activate
pip3 install -r requirements.txt

Dataset

Currently this repo only supports IEMOCAP.

Arguments

The definitions of the args are described in ded/arguments.py. You can modify all args there.

Usage

python3 main.py --verbosity 1 --result_file RESULT_FILE

Results

Results of DED with beam size = 5.

Model	UAR	ACC
Pretrained Classifier	0.671	0.653
DED	0.710	0.695

Oral Presentation