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Register a new userIncremental Few-shot Text Classification with Multi-round New Classes: Formulation, Dataset and System
تصنيف نص قليل قصير بالرصاص مع فصول جديدة متعددة جولات: صياغة ومجموعات البيانات والنظام
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عادة ما تتم دراسة تصنيف النص عن طريق وضع علامات نصوص اللغة الطبيعية مع الفئات ذات الصلة من مجموعة محددة مسبقا. في العالم الحقيقي، قد تستمر فصول جديدة في تحدي النظام الحالي مع بيانات محدودة المسمى. يجب أن يكون النظام ذكي بما يكفي للتعرف على الطبقات الجديدة القادمة مع بعض الأمثلة. في هذا العمل، نحدد مهمة جديدة في مجال NLP، تصنيف النص قليل الطوابق الإضافي، حيث يتعامل النظام تدريجيا جولات متعددة من الفصول الجديدة. لكل جولة، هناك مجموعة من الطبقات الجديدة مع بعض الأمثلة المسمى لكل فصل. يوجد تحديان رئيسيان في هذه المهمة الجديدة: (1) لعملية التعلم، يجب أن يتعلم النظام تدريجيا على جولة فصول جديدة جولة من الجولة دون إعادة التدريب على الأمثلة على الطبقات السابقة؛ (2) بالنسبة للأداء، يجب أن يؤدي النظام بشكل جيد على فئات جديدة دون فقدان الكثير في الفصول السابقة. بالإضافة إلى صياغة المهمة الجديدة، نقوم أيضا بإصدار مجموعة بيانات قياسية في الإعداد القليل من الرصاص الإضافي: تصنيف النوايا وتصنيف العلاقات. علاوة على ذلك، نقترح اثنين مناهج استقصاء وتتبعها والجاذبية، والتي تظهر الوعد بحل هذه المشكلة الرواية.
Text classification is usually studied by labeling natural language texts with relevant categories from a predefined set. In the real world, new classes might keep challenging the existing system with limited labeled data. The system should be intelligent enough to recognize upcoming new classes with a few examples. In this work, we define a new task in the NLP domain, incremental few-shot text classification, where the system incrementally handles multiple rounds of new classes. For each round, there is a batch of new classes with a few labeled examples per class. Two major challenges exist in this new task: (i) For the learning process, the system should incrementally learn new classes round by round without re-training on the examples of preceding classes; (ii) For the performance, the system should perform well on new classes without much loss on preceding classes. In addition to formulating the new task, we also release two benchmark datasets in the incremental few-shot setting: intent classification and relation classification. Moreover, we propose two entailment approaches, ENTAILMENT and HYBRID, which show promise for solving this novel problem.
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