Polyglot Prompt: Multilingual Multitask PrompTraining
This addresses the challenge of multilingual learning for low-resource systems by enabling cross-language and cross-task assistance, though it appears incremental as it builds on existing prompting methods.
The paper tackles the problem of modeling different tasks from different languages in a monolithic framework without task/language-specific modules, achieving efficacy in multilingual multitask prompt-based learning across 6 tasks, 24 datasets, and 49 languages.
This paper aims for a potential architectural improvement for multilingual learning and asks: Can different tasks from different languages be modeled in a monolithic framework, i.e. without any task/language-specific module? The benefit of achieving this could open new doors for future multilingual research, including allowing systems trained on low resources to be further assisted by other languages as well as other tasks. We approach this goal by developing a learning framework named Polyglot Prompting to exploit prompting methods for learning a unified semantic space for different languages and tasks with multilingual prompt engineering. We performed a comprehensive evaluation of 6 tasks, namely topic classification, sentiment classification, named entity recognition, question answering, natural language inference, and summarization, covering 24 datasets and 49 languages. The experimental results demonstrated the efficacy of multilingual multitask prompt-based learning and led to inspiring observations. We also present an interpretable multilingual evaluation methodology and show how the proposed framework, multilingual multitask prompt training, works. We release all datasets prompted in the best setting and code.