WU_TLAXE at WASSA 2024 Explainability for Cross-Lingual Emotion in Tweets Shared Task 1: Emotion through Translation using TwHIN-BERT and GPT
Jon Davenport, Keren Ruditsky, Anna Batra, Yulha Lhawa, Gina‐Anne Levow · 2024
This paper describes our task 1 submission for the WASSA 2024 shared task on Explainability for Cross-lingual Emotion in Tweets.Our task is to predict the correct emotion label (Anger, Sadness, Fear, Joy, Love, and Neutral) for a dataset of English, Dutch, French, Spanish, and Russian tweets, while training exclusively on English emotion labeled data, to reveal what kind of emotion detection information is transferable cross-language (Maladry et al., 2024).To that end, we used an ensemble of models with a GPT-4 decider.Our ensemble consisted of a few-shot GPT-4 prompt system and a TwHIN-BERT system fine-tuned on the EX-ALT and additional English data.We ranked 8th place under the name WU_TLAXE with an F1 Macro score of 0.573 on the test set.We also experimented with an English-only TwHIN-BERT model by translating the other languages into English for inference, which proved to be worse than the other models.