MIME

This repository contains PyTorch implementation of empathetic-response generation model MIME: MIMicking Emotions for Empathetic Response Generation.

Overview of MIME

Current approaches to empathetic response generation view the set of emotions expressed in the input text as a flat structure, where all the emotions are treated uniformly. We argue that empathetic responses often mimic the emotion of the user to a varying degree, depending on its positivity or negativity and content. We show that the consideration of these polarity-based emotion clusters and emotional mimicry results in improved empathy and contextual relevance of the response as compared to the state-of-the-art. Also, we introduce stochasticity into the emotion mixture that yields emotionally more varied empathetic responses than the previous work. We demonstrate the importance of these factors to empathetic response generation using both automatic- and human-based evaluations.

Setup

Download GloVe vectors from here and put it into vectors/ folder

Next Install the required libraries:

Assume you are using conda
Install libraries by pip install -r requirement.txt

For reproducibility purposes, we provide model output on test dataset as ./output.txt and weights in google drive. you can download the model and move it under save/saved_model

Run code

Dataset is already preprocessed and contained in this repo. we used proprocessed data provided in this link https://github.com/HLTCHKUST/MoEL/tree/master/empathetic-dialogue.

Training

python main.py

Note: This will also generate output file on test dataset as save/test/output.txt.

Testing

python main.py --test --save_path [output_file_path]

Note: During testing, the model will load weight under save/saved_model, and by default it will generate save/test/output.txt as output file on test dataset.

Citation

MIME: MIMicking Emotions for Empathetic Response Generation. Navonil Majumder, Pengfei Hong, Shanshan Peng, Jiankun Lu, Deepanway Ghosal, Alexander Gelbukh, Rada Mihalcea, Soujanya Poria. EMNLP (2020).

suhasagg/MIME