Empathy and Distress Detection using Ensembles of Transformer Models
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Abstract (arXiv)
This paper presents our approach for the WASSA 2023 Empathy, Emotion and Personality Shared Task. Empathy and distress are human feelings that are implicitly expressed in natural discourses. Empathy and distress detection are crucial challenges in Natural Language Processing that can aid our understanding of conversations. The provided dataset consists of several long-text examples in the English language, with each example associated with a numeric score for empathy and distress. We experiment with several BERT-based models as a part of our approach. We also try various ensemble methods. Our final submission has a Pearson's r score of 0.346, placing us third in the empathy and distress detection subtask.
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References · 7
- 2017Attention Is All You Needcited by 59,932
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2021SimCSE: Simple Contrastive Learning of Sentence Embeddingscited by 2,168
- 2020TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classificationcited by 414
- 2022Empathic Conversations: A Multi-level Dataset of Contextualized Conversationscited by 20
- 2021EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Predictioncited by 17
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