Transformer based ensemble for emotion detection
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Abstract (arXiv)
Detecting emotions in languages is important to accomplish a complete interaction between humans and machines. This paper describes our contribution to the WASSA 2022 shared task which handles this crucial task of emotion detection. We have to identify the following emotions: sadness, surprise, neutral, anger, fear, disgust, joy based on a given essay text. We are using an ensemble of ELECTRA and BERT models to tackle this problem achieving an F1 score of 62.76%. Our codebase (https://bit.ly/WASSAsharedtask) and our WandB project (https://wandb.ai/aclwassapictxmanipal/aclwassa) is publicly available.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 22 Mar 2022.
Builds on
9 of 14 references.
References · 9
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- 2020GoEmotions: A Dataset of Fine-Grained Emotionscited by 486
- 2021SpanEmo: Casting Multi-label Emotion Classification as Span-predictioncited by 37
- 2021WASSA@IITK at WASSA 2021: Multi-task Learning and Transformer Finetuning for Emotion Classification and Empathy Predictioncited by 6
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