wassa-2023-emo

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This model detects multiple emotions from user essays (texts). Used in the paper "RoBERTa-Based Multi-class Emotion detection on highly imbalanced data" (ACL 2023)

Model Details

Multi-way Multi-class Emotion classification from user texts finetuned on WASSA 2023 dataset on roberta-large

Model Description

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This paper presents a study on using the RoBERTa language model for emotion classification of essays as part of the ’Shared Task on Empathy Detection, Emotion Classification and Personality Detection in Interactions’ (Barriere et al., 2023), organized as part of ’WASSA 2023’ at ’ACL 2023’. Emotion classification is a challenging task in natural language processing, and imbalanced datasets further exacerbate this challenge. In this study, we explore the use of various data balancing techniques in combination with RoBERTa (Liu et al., 2019) to improve the classification performance. We evaluate the performance of our approach (denoted by adityapatkar on Codalab (Pavao et al.,2022)) on a multi-label dataset of essays annotated with eight emotion categories, provided by the Shared Task organizers. Our results show that the proposed approach achieves the best macro F1 score in the competition’s training and evaluation phase. Our study provides insights into the potential of RoBERTa for handling imbalanced data in emotion classification. The results can have implications for the natural language processing tasks related to emotion classification.

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• Language(s) (NLP): English (EN)
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• Finetuned from model [optional]: Facebook/roberta-large

Model Sources [optional]

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• Repository: https://github.com/1024-m/ACL-2023-WASSA-TASK-3
• Paper [optional]: https://www.rkadiyala.com/papers
• Demo [optional]: [More Information Needed]

Uses

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Direct Use

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Results

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Summary

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Model Architecture and Objective

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Compute Infrastructure

1x V100 GPU 16RAM

Hardware

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Software

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Citation [optional]

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Aditya Patkar, Suraj Chandrashekhar, and Ram Mohan Rao Kadiyala. 2023. AdityaPatkar at WASSA 2023 Empathy, Emotion, and Personality Shared Task: RoBERTa-Based Emotion Classification of Essays, Improving Performance on Imbalanced Data. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 531–535, Toronto, Canada. Association for Computational Linguistics.

BibTeX:

@inproceedings{ patkar-etal-2023-adityapatkar, title = "{A}ditya{P}atkar at {WASSA} 2023 Empathy, Emotion, and Personality Shared Task: {R}o{BERT}a-Based Emotion Classification of Essays, Improving Performance on Imbalanced Data", author = "Patkar, Aditya and Chandrashekhar, Suraj and Kadiyala, Ram Mohan Rao", editor = "Barnes, Jeremy and De Clercq, Orph{\'e}e and Klinger, Roman", booktitle = "Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, {\&} Social Media Analysis", month = jul, year = "2023", address = "Toronto, Canada", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2023.wassa-1.46", doi = "10.18653/v1/2023.wassa-1.46", pages = "531--535", }

APA:

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Glossary [optional]

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Model Card Contact

https://www.rkadiyala.com