Adapter-Tuning for Empathy Prediction

Experimental code for CAISA at WASSA 2022: Adapter-Tuning for Empathy Prediction

Citation

If you use our work, please cite our paper

@inproceedings{lahnala-etal-2022-caisa,
    title = "{CAISA} at {WASSA} 2022: Adapter-Tuning for Empathy Prediction",
    author = "Lahnala, Allison  and
      Welch, Charles  and
      Flek, Lucie",
    booktitle = "Proceedings of the 12th Workshop on Computational Approaches to Subjectivity, Sentiment {\&} Social Media Analysis",
    month = may,
    year = "2022",
    address = "Dublin, Ireland",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.wassa-1.31",
    doi = "10.18653/v1/2022.wassa-1.31",
    pages = "280--285",
    abstract = "We build a system that leverages adapters, a light weight and efficient method for leveraging large language models to perform the task Em- pathy and Distress prediction tasks for WASSA 2022. In our experiments, we find that stacking our empathy and distress adapters on a pre-trained emotion lassification adapter performs best compared to full fine-tuning approaches and emotion feature concatenation. We make our experimental code publicly available",
}

EmotionStack

Empathy_Distress_Inference.ipynb: Code to use pretrained empathy and distress adapters (stacked on emotion adapter) to predict empathy and distress scores.

EmotionStack_EMP.ipynb: Code for predicting the empathy and distress at essay level using the EmotionStack approach.

EmotionStack_EMO.ipynb: Code for predicting the emotion labels at essay level using the EmotionStack approach.

EpitomeFusion

Train_Empathy_Adapters.ipynb: Code for training the adapters on each of the EPITOME 1 classes of empathy. You can obtain the epitome dataset here.

EpitomeFusion.ipynb: Code for predicting the emotion labels at essay level using the EpitomeFusion approach.

Adapters

The adapters we trained for the EMP and EMO tasks are in the trained_adapters folder. See Empathy_Distress_Inference.ipynb as an example of how to load and use them for inference.

Predictions

The predictions for distress, empathy, and emotion on the test set are located in the predictions folder.

References

[1] Sharma, Ashish, et al. "A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support." Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020.