Empathify at WASSA 2024 Empathy and Personality Shared Task: Contextualizing Empathy with a BERT-Based Context-Aware Approach for Empathy Detection

Date

2024-01-01

Author

Numanoğlu, Arda
Ateş, Süleyman
Çiçekli, Fehime Nihan
Küçük, Dilek

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Empathy detection from textual data is a complex task that requires an understanding of both the content and context of the text. This study presents a BERT-based context-aware approach to enhance empathy detection in conversations and essays. We participated in the WASSA 2024 Shared Task (Giorgi et al., 2024), focusing on two tracks: empathy and emotion prediction in conversations (CONV-turn) and empathy and distress prediction in essays (EMP). Our approach leverages contextual information by incorporating related articles and emotional characteristics as additional inputs, using BERT-based Siamese (parallel) architecture. Our experiments demonstrated that using article summaries as context significantly improves performance, with the parallel BERT approach outperforming the traditional method of concatenating inputs with the ‘[SEP]‘token. These findings highlight the importance of context-awareness in empathy detection and pave the way for future improvements in the sensitivity and accuracy of such systems. Our system officially ranked 8th at both CONV-T and EMP tracks.

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https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85204910192&origin=inward
https://hdl.handle.net/11511/112098

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Department of Computer Engineering, Conference / Seminar