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

• URL: http://arxiv.org/abs/2103.06057v1
• Date: Wed, 10 Mar 2021 14:00:54 GMT
• Title: Team Phoenix at WASSA 2021: Emotion Analysis on News Stories with Pre-Trained Language Models
• Authors: Yash Butala, Kanishk Singh, Adarsh Kumar and Shrey Shrivastava
• Abstract summary: We describe our system entry for the WASSA 2021 Shared Task. 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.
• Score: 1.6536018920603175
• License: http://creativecommons.org/licenses/by/4.0/
• 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 4th rank in Track 1 and 2nd rank in Track 2.

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