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Register a new userAnalyzing the Effects of Reasoning Types on Cross-Lingual Transfer Performance
تحليل آثار أنواع المنطق على أداء النقل عبر اللغات
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تحدث نماذج لغة متعددة اللغات بدقة مثيرة للإعجاب بدقة في العديد من اللغات في مهام معقدة مثل الاستدلال اللغوي الطبيعي (NLI).غالبا ما تتعلق أمثلة في المهام المعقدة المكافئة (وما يعادلها) أنواعا مختلفة من المهام الفرعية، والتي تتطلب أنواعا مختلفة من التفكير.لقد أثبتت أنواع معينة من التفكير أكثر صعوبة في التعلم في سياق أحادي الأونلينغ، وفي السياق crosslingual، قد تسليف الملاحظات المماثلة الضوء على كفاءة نقل صفرية ومختيار عينة قليلة.وبالتالي، للتحقيق في آثار أنواع المنطق في أداء النقل، نقترح مجموعة بيانات NLI متعددة اللغات متعددة الفئات ومناقشة التحديات اللازمة لتوسيع نطاق التعليقات التوضيحية أحادية الأونلينغ إلى لغات متعددة.نلاحظ إحصائيا تأثيرات مثيرة للاهتمام أن التقاء أنواع المنطق وأشابه لغة لها على أداء نقل.
Multilingual language models achieve impressive zero-shot accuracies in many languages in complex tasks such as Natural Language Inference (NLI). Examples in NLI (and equivalent complex tasks) often pertain to various types of sub-tasks, requiring different kinds of reasoning. Certain types of reasoning have proven to be more difficult to learn in a monolingual context, and in the crosslingual context, similar observations may shed light on zero-shot transfer efficiency and few-shot sample selection. Hence, to investigate the effects of types of reasoning on transfer performance, we propose a category-annotated multilingual NLI dataset and discuss the challenges to scale monolingual annotations to multiple languages. We statistically observe interesting effects that the confluence of reasoning types and language similarities have on transfer performance.
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