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Register a new userSingle Example Can Improve Zero-Shot Data Generation
مثال واحد يمكن أن يحسن توليد البيانات صفر
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تتطلب المهام الفرعية لتصنيف النية، مثل التواضع على تحول التوزيع، والتكيف مع مجموعات المستخدمين المعينة والتخصيص، والكشف خارج المجال، ومجموعات بيانات واسعة ومرنة للتجارب والتقييم.نظرا لأن جمع مجموعات البيانات هذه هي الوقت والمستهلك للعمل، نقترح استخدام أساليب جيل النص لجمع البيانات.يجب تدريب المولد على توليد الكلام التي تنتمي إلى نية معينة.نستكشف مناهضين لتوليد الكلام الموجهين في المهام: في نهج الطلقة الصفرية، يتم تدريب النموذج على توليد الكلام من النوايا المشاهدة ويتم استخدامها أيضا لتوليد الكلام للمحاطة غير المرئية أثناء التدريب.في نهج طلقة واحدة، يتم تقديم النموذج مع كلام واحد من نية الاختبار.نحن نؤدي التقييم التلقائي الشامل والبشري للخصائص الجوهرية لنهج الجيلين.يتم تصنيف سمات البيانات التي تم إنشاؤها من مجموعات الاختبار الأصلية، التي تم جمعها عبر مصادر الحشد.
Sub-tasks of intent classification, such as robustness to distribution shift, adaptation to specific user groups and personalization, out-of-domain detection, require extensive and flexible datasets for experiments and evaluation. As collecting such datasets is time- and labor-consuming, we propose to use text generation methods to gather datasets. The generator should be trained to generate utterances that belong to the given intent. We explore two approaches to the generation of task-oriented utterances: in the zero-shot approach, the model is trained to generate utterances from seen intents and is further used to generate utterances for intents unseen during training. In the one-shot approach, the model is presented with a single utterance from a test intent. We perform a thorough automatic, and human evaluation of the intrinsic properties of two-generation approaches. The attributes of the generated data are close to original test sets, collected via crowd-sourcing.
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