Emotion Stimulus Detection in German News Headlines
This work provides a resource and baseline models for emotion stimulus detection in German, addressing a gap for NLP applications in German-language contexts, but it is incremental as it extends existing methods to a new language.
The paper addressed the lack of resources for emotion stimulus detection in German by creating a corpus of 2006 annotated news headlines and 811 stimulus instances, and found that training on the German corpus outperformed cross-lingual projection methods, with XLM-R models achieving higher F1 scores than CRF models.
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.