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English Prompts Outperform Target-Language Prompts for Emotion Classification in NLI-based Models

Core Concepts
The author argues that using English prompts is more effective than target-language prompts for emotion classification in multilingual NLI models, based on experimental results.
The content discusses the challenges of emotion classification in text and the domain-specific nature of emotion categories. It highlights the importance of zero-shot classifications and the research gap regarding prompting language models for non-English texts. The experiments with natural language inference-based models show consistent better performance using English prompts even with data in a different language. The paper addresses questions about transferring prompts for zero-shot emotion classification across languages, analyzing prompt types' stability, and consistency across different NLI models.
"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." "For some data sets and languages, the performance is lower than for others, which we interpret as a varying difficulty of the respective data sets." "We conclude that it is generally better or equally beneficial to use an English prompt for performing emotion classification in a target language."

Deeper Inquiries

How can researchers address biases towards English prompts in multilingual language models?

Researchers can address biases towards English prompts in multilingual language models by exploring various strategies: Diversifying Training Data: Including more data from different languages during the pretraining phase can help reduce the bias towards English prompts. Language-Agnostic Prompts: Developing prompt templates that are less dependent on specific languages can mitigate the bias. These prompts should focus on universal linguistic features rather than language-specific nuances. Fine-Tuning on Multilingual Data: Fine-tuning the model on a diverse set of multilingual datasets can help it adapt better to different languages, reducing reliance on English prompts. Prompt Translation Strategies: Experimenting with different translation techniques for adapting English prompts to target languages, ensuring accurate and culturally appropriate translations.

What implications do these findings have for cross-lingual transfer learning beyond emotion classification?

The findings suggest several implications for cross-lingual transfer learning beyond emotion classification: Generalizability Across Tasks: The preference for English prompts may extend to other NLP tasks, indicating that using English as a common prompt language could improve performance across various applications. Efficiency in Model Deployment: Leveraging well-performing English prompts for target languages can streamline model deployment and reduce the need for task-specific adaptations in low-resource settings. Consistency in Performance: Understanding the robustness of certain prompt types and languages across models highlights potential generalizable patterns that could enhance cross-lingual transfer learning efficacy.

How can future research explore prompt adaptation strategies to improve performance on target languages?

Future research could delve into innovative approaches to enhance performance on target languages through prompt adaptation strategies: Dynamic Prompt Generation: Developing dynamic prompting mechanisms that adjust based on linguistic characteristics of the input text or target language could optimize model responses effectively. Multimodal Prompting Techniques: Integrating multimodal cues such as images or audio alongside text-based prompts may offer richer context cues, improving understanding and accuracy in target-language processing. Adversarial Prompt Tuning: Exploring adversarial training methods specifically tailored for prompt adaptation might help fine-tune models efficiently across multiple languages while mitigating biases introduced by fixed-language prompting structures. These avenues of exploration hold promise for advancing cross-lingual transfer learning capabilities and optimizing model performance across diverse linguistic contexts beyond emotion classification tasks.