Emotion Stimulus Detection in German News Headlines

TL;DR

This paper introduces a German news headline corpus with emotion and stimulus annotations, compares monolingual and cross-lingual models, and demonstrates the superiority of multilingual XLM-RoBERTa in emotion stimulus detection.

Contribution

It creates the first German emotion stimulus dataset and evaluates monolingual and cross-lingual models, showing XLM-R's effectiveness over CRF models.

Findings

German corpus improves F1 scores over projection methods.

XLM-R outperforms CRF models in emotion stimulus detection.

Cross-lingual projection is less effective than training on native data.

Abstract

Emotion stimulus extraction is a fine-grained subtask of emotion analysis that focuses on identifying the description of the cause behind an emotion expression from a text passage (e.g., in the sentence "I am happy that I passed my exam" the phrase "passed my exam" corresponds to the stimulus.). Previous work mainly focused on Mandarin and English, with no resources or models for German. We fill this research gap by developing a corpus of 2006 German news headlines annotated with emotions and 811 instances with annotations of stimulus phrases. Given that such corpus creation efforts are time-consuming and expensive, we additionally work on an approach for projecting the existing English GoodNewsEveryone (GNE) corpus to a machine-translated German version. We compare the performance of a conditional random field (CRF) model (trained monolingually on German and cross-lingually via projection) with a multilingual XLM-RoBERTa (XLM-R) model. Our results show that training with the German corpus achieves higher F1 scores than projection. Experiments with XLM-R outperform their respective CRF counterparts.