HIT - SCIR at WASSA 2023: Empathy and Emotion Analysis at the Utterance-Level and the Essay-Level (2023)

Abstract: This paper introduces the participation of team HIT-SCIR to the WASSA 2023 Shared Task on Empathy Detection and Emotion Classification and Personality Detection in Interactions. We focus on three tracks: Track 1 (Empathy and Emotion Prediction in Conversations, CONV), Track 2 (Empathy Prediction, EMP) and Track 3 (Emotion Classification, EMO), and designed three different models to address them separately. For Track 1, we designed a direct fine-tuning DeBERTa model for three regression tasks at the utterance-level. For Track 2, we designed a multi-task learning RoBERTa model for two regression tasks at the essay-level. For Track 3, we designed a RoBERTa model with data augmentation for the classification task at the essay-level. Finally, our team ranked 1st in the Track 1 (CONV), 5th in the Track 2 (EMP) and 3rd in the Track 3 (EMO) in the evaluation phase.

## introduction in the field of human-computer interaction systems, a discernible trend is the increased focus on the emotion and empathy status of users and the facilitation of emotional exchanges with them. this approach significantly contributes to enhancing service quality and boosting user satisfaction. however, analyzing the emotion and empathy status of users is still a challenging problem, which requires researchers to conduct thorough exploration and in-depth study. the wassa 2023 shared task 1 @xcite provides a unified evaluation benchmark, on the basis of which we have conducted corresponding work. we have participated in three of five tracks, which are: track 1: empathy and emotion prediction in conversations (conv), which consists in predicting the perceived empathy, emotion polarity and emotion intensity at the utterance-level in a dialog. track 2: empathy prediction (emp), which consists in predicting both the empathy concern and the personal distress at the essay-level. track 3: emotion classification (emo), which consists in predicting the emotion at the essaylevel. we conducted analyses and experiments on these three tracks concurrently. in section 2, we present the methodologies designed for different tasks, the dataset features used in our design and our ensembling method. in section 3, we introduce the experimental results of our proposed methods, along with corresponding result analyses. in section 4, we provide our conclusions and summarize our methodologies. the implementation details can be found in appendix a. ## conclusion our team hit-scir participated in the wassa 2023 shared task on empathy detection and emotion classification and personality detection in interactions. we focused on empathy and emotion analysis and participated in three of five tracks. we analyzed the features of each task and designed different methodologies for them. finally, our team ranked 1st in the track 1 (conv), 5th in the track 2 (emp) and 3rd in the track 3 (emo) in the evaluation phase. the optimizer used for model training is adamw @xcite optimizer which is a fixed version of adam (kingma and ba, 2014) with weight decay, and set β 1 to 0.9, β 2 to 0.99 for the optimizer. all experiments select the best parameters in the valid set and then use the ensembling method in 2.4 . below is the details to the custom parameter settings for different tracks. for conv track, we use both the deberta-v2xl and deberta-v2-xxl as our pre-trained models. for each metric of this track (polarity, intensity and empathy), we fine-tune on both models. specifically, during the process of choosing the best window size, for each window size, we fine-tune on both models. the deberta model comes with 24(48) layers and a hidden size of 1536. the total parameters are 900m(1.5b), and it is trained with 160gb raw data. deberta improves the bert and roberta models using two novel techniques, the disentangled attention mechanism and the enhanced mask decoder. we use the learning rate {3e-6, 4e-6}, total training batch size 32, training epoch 6 for deberta-xl and deberta-xxl separately. we conduct distributed training on 4 nvidia a100-80gb gpus and use fp16 training. we set the maximum length of 1024, and delete the excess. for emp track, we use the roberta-base as our pre-trained model, and fine-tune the model. roberta @xcite extends bert @xcite by incorporating techniques like dynamic masking and removing the next sentence prediction pre-training objective. we conduct grid search with the learning rate varying in {1e-5, 2e-5, 3e-5}, batch size varying in {8, 16, 32}, and dropout rate varying in {0, 0.3}. we set the maximum length of 512, and delete the excess. for emo track, we use the roberta-large as our pre-trained model, and fine-tune the model. we conduct grid search with the same parameter search range as that for the emp track. additionally, the hidden dimension of the bilstm layer is 256.