English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts
<|reference_start|>English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts: Emotion classification in text is a challenging task due to the processes involved when interpreting a textual description of a potential emotion stimulus. In addition, the set of emotion categories is highly domain-specific. For instance, literature analysis might require the use of aesthetic emotions (e.g., finding something beautiful), and social media analysis could benefit from fine-grained sets (e.g., separating anger from annoyance) than only those that represent basic categories as they have been proposed by Paul Ekman (anger, disgust, fear, joy, surprise, sadness). This renders the task an interesting field for zero-shot classifications, in which the label set is not known at model development time. Unfortunately, most resources for emotion analysis are English, and therefore, most studies on emotion analysis have been performed in English, including those that involve prompting language models for text labels. This leaves us with a research gap that we address in this paper: In which language should we prompt for emotion labels on non-English texts? This is particularly of interest when we have access to a multilingual large language model, because we could request labels with English prompts even for non-English data. Our experiments with natural language inference-based language models show that it is consistently better to use English prompts even if the data is in a different language.<|reference_end|>
@article{bareiß2024english,
    title={English Prompts are Better for NLI-based Zero-Shot Emotion
  Classification than Target-Language Prompts},
    author={Patrick Barei{ss} and Roman Klinger and Jeremy Barnes},
    journal={arXiv preprint arXiv:2402.03223},
    year={2024},
    doi={10.1145/3589335.3651902},
    archivePrefix={arXiv},
    eprint={2402.03223},
    primaryClass={cs.CL}
}