Team Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Models

Yash Butala, Kanishk Singh, Adarsh Kumar, Shrey Shrivastava


Abstract
Emotion is fundamental to humanity. The ability to perceive, understand and respond to social interactions in a human-like manner is one of the most desired capabilities in artificial agents, particularly in social-media bots. Over the past few years, computational understanding and detection of emotional aspects in language have been vital in advancing human-computer interaction. The WASSA Shared Task 2021 released a dataset of news-stories across two tracks, Track-1 for Empathy and Distress Prediction and Track-2 for Multi-Dimension Emotion prediction at the essay-level. We describe our system entry for the WASSA 2021 Shared Task (for both Track-1 and Track-2), where we leveraged the information from Pre-trained language models for Track-specific Tasks. Our proposed models achieved an Average Pearson Score of 0.417, and a Macro-F1 Score of 0.502 in Track 1 and Track 2, respectively. In the Shared Task leaderboard, we secured the fourth rank in Track 1 and the second rank in Track 2.
Anthology ID:
2021.wassa-1.30
Volume:
Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Month:
April
Year:
2021
Address:
Online
Venues:
EACL | WASSA
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
274–280
Language:
URL:
https://aclanthology.org/2021.wassa-1.30
DOI:
Bibkey:
Cite (ACL):
Yash Butala, Kanishk Singh, Adarsh Kumar, and Shrey Shrivastava. 2021. Team Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Models. In Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, pages 274–280, Online. Association for Computational Linguistics.
Cite (Informal):
Team Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Models (Butala et al., WASSA 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.wassa-1.30.pdf
Optional supplementary material:
 2021.wassa-1.30.OptionalSupplementaryMaterial.zip
Code
 yashbutala/WASSA
Data
CARER