mlsanityData to 14 Oct 2026

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

arXiv 2103.06057 ↗First posted 10 Mar 2021cs.CLcited by 5

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Abstract (arXiv)

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.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 22 of its 35 references that are in the map. First posted on arXiv 10 Mar 2021.

Builds on

22 of 35 references.

References · 22

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10 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 2.

5 papers.

  1. 2023Systematic Evaluation of GPT-3 for Zero-Shot Personality Estimationcited by 29
  2. 2023Empathy Detection from Text, Audiovisual, Audio or Physiological Signals: A Systematic Review of Task Formulations and Machine Learning Methodscited by 5
  3. 2022A Critical Reflection and Forward Perspective on Empathy and Natural Language Processingcited by 4
  4. 2025Labels Generated by Large Language Models Help Measure People's Empathy in Vitrocited by 1
  5. 2025UPLME: Uncertainty-Aware Probabilistic Language Modelling for Robust Empathy Regressioncited by 1