NCUEE - NLP at WASSA 2023 Shared Task 1: Empathy and Emotion Prediction Using Sentiment-Enhanced R o BERT a Transformers (2023)

Abstract: This paper describes our proposed system design for the WASSA 2023 shared task 1. We propose a unified architecture of ensemble neural networks to integrate the original RoBERTa transformer with two sentiment-enhanced RoBERTa-Twitter and EmoBERTa models. For Track 1 at the speech-turn level, our best submission achieved an average Pearson correlation score of 0.7236, ranking fourth for empathy, emotion polarity and emotion intensity prediction. For Track 2 at the essay-level, our best submission obtained an average Pearson correlation score of 0.4178 for predicting empathy and distress scores, ranked first among all nine submissions.

## introduction empathy is the capacity to understand or feel what another person is experiencing from his/her perspectives, which is a cognitive and emotional reaction to observing the situation of others @xcite . computational detection and prediction of empathy has attracted considerable attention in recent years. empathy assessment by the writer of a statement was captured and annotated to computationally distinguish between multiple forms of empathy, empathic concerns and personal distress @xcite . mixed-level feed forward network (mlffn) was proposed to learn word ratings for empathy and distress @xcite . logistic regression models were used to recognize distress and condolences reactions to such distress @xcite . a multi-task roberta-based bi-encoder model was developed to identify empathy in conversations @xcite . a demographic-aware empathbert architecture was presented to infuse demographic information for empathy prediction @xcite . the workshop on computational approaches to subjectivity, sentiment and social media analysis (wassa) organizes shared tasks for different aspects of affect computation from texts. in wassa 2021 and 2022 shared tasks that focus on predicting empathy and emotion in reaction to news stories @xcite , using transformers based pre-trained language models to achieve promising results. the pvg team proposed a multi-input and multi-task framework based on the roberta transformer for empathy score prediction @xcite ). an ensemble of the roberta multi-task model and the vanilla electra model was used to predict empathy scores @xcite . the iucl system fine-tuned two roberta large models, including a regression model for empathy and distress prediction and a classification model for emotion detection @xcite . a multioutput regression model fine-tuned by roberta with additional features, including gender, income and age was used to predict empathy and distress intensity @xcite . the task adapters for a roberta model were trained to predict empathy and distress scores at the essay-level @xcite . wassa-2023 organizes a similar task with a newly added track on empathy, emotion and selfdisclosure detection in conversation at the speechturn level @xcite . we participated in the track 1 for empathy and emotion prediction in conversations (conv), aiming to predict perceived empathy, emotion polarity and emotion ## the ncuee-nlp system we propose a unified architecture of ensemble neural networks to solve tracks 1 and 2 of wassa-2023 shared task 1. figure 1 shows our system architecture for empathy and emotion prediction, which mainly depends on ensemble sentiment-enhanced transformers. we select the following roberta-based transformers to tackle both tracks in this task. for both tracks in this shared task, we fine-tune these pre-trained roberta-based transformers using the datasets provided by task organizers. for track 1 on empathy and emotion prediction in conversations, we separately fine-tuned these transformers for empathy, emotion polarity and emotion intensity prediction. for track 2 on empathy prediction at the essay-level, we respectively trained the transformers for empathy and distress score prediction. finally, we use the average ensemble mechanism to combine these individual sentimentenhanced roberta transformer to produce a desired score output for both tracks. ## conclusions this study describes the model design, system implementation and performance of the ncuee-nlp system in the wassa 2023 task 1 for empathy and emotion prediction. our unified architecture used an average ensemble mechanism of three sentiment-enhanced roberta transformers to predict empathy, emotion polarity and emotion intensity for track 1 at the speech-turn level and empathy and distress scores for track 2 at the essay-level. our final submission based on sentiment-enhanced roberta transformers ranked fourth for track 1 and first for track 2 among all nine submissions.