Cross-lingual Emotion Intensity Prediction
This work addresses the problem of fine-grained emotion detection for low-resource languages like Spanish and Catalan, offering a dataset and methods to reduce annotation burdens, though it is incremental as it builds on existing cross-lingual techniques.
The paper tackled cross-lingual emotion intensity prediction for Spanish and Catalan tweets by comparing six transfer approaches with varying parallel data needs, finding that low-data methods outperformed high-data ones, with specific performance gains detailed in the error analysis.
Emotion intensity prediction determines the degree or intensity of an emotion that the author expresses in a text, extending previous categorical approaches to emotion detection. While most previous work on this topic has concentrated on English texts, other languages would also benefit from fine-grained emotion classification, preferably without having to recreate the amount of annotated data available in English in each new language. Consequently, we explore cross-lingual transfer approaches for fine-grained emotion detection in Spanish and Catalan tweets. To this end we annotate a test set of Spanish and Catalan tweets using Best-Worst scaling. We compare six cross-lingual approaches, e.g., machine translation and cross-lingual embeddings, which have varying requirements for parallel data -- from millions of parallel sentences to completely unsupervised. The results show that on this data, methods with low parallel-data requirements perform surprisingly better than methods that use more parallel data, which we explain through an in-depth error analysis. We make the dataset and the code available at \url{https://github.com/jerbarnes/fine-grained_cross-lingual_emotion}