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Register a new userSynthetic Examples Improve Cross-Target Generalization: A Study on Stance Detection on a Twitter corpus.
الأمثلة الاصطناعية تعمل على تحسين التعميم الشامل: دراسة عن اكتشاف الموقف على Twitter Corpus.
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تعد التعميم المرتبطة مشكلة معروفة في الكشف عن الموقف (SD)، حيث تميل النظم إلى الأداء بشكل سيئ عند تعرضها للأهداف غير المرئية أثناء التدريب.بالنظر إلى أن شرح البيانات باهظ الثمن وتستغرق وقتا طويلا، فإن إيجاد طرق للاستفادة من البيانات غير المستقرة غير المسبقة يمكن أن تقدم فوائد كبيرة.في هذه الورقة، نطبق إطارا إشرافه ضعيفا لتعزيز التعميم الشامل من خلال البيانات المشروحة بتهمة التوحيد.نحن نركز على Twitter SD وإظهار تجريبيا من أن دمج البيانات الاصطناعية مفيدة للتعميم الشامل، مما يؤدي إلى تحسينات كبيرة في الأداء، مع المكاسب في درجات F1 تتراوح بين +3.4 إلى +5.1.
Cross-target generalization is a known problem in stance detection (SD), where systems tend to perform poorly when exposed to targets unseen during training. Given that data annotation is expensive and time-consuming, finding ways to leverage abundant unlabeled in-domain data can offer great benefits. In this paper, we apply a weakly supervised framework to enhance cross-target generalization through synthetically annotated data. We focus on Twitter SD and show experimentally that integrating synthetic data is helpful for cross-target generalization, leading to significant improvements in performance, with gains in F1 scores ranging from +3.4 to +5.1.
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