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Register a new userAspect-Controlled Neural Argument Generation
جيل الحجة العصبية التي تسيطر عليها الجانب
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نحن نعتمد على الحجج في حياتنا اليومية لتسليم آرائنا وتساعدهم على الأدلة، مما يجعلها أكثر إقناعا بدورها.ومع ذلك، يمكن أن يكون العثور على والحجج وصياغة التحدي.في هذا العمل، نقدم Arg-Ctrl - نموذج لغوي لتوليد الوسيطة الذي يمكن التحكم فيه لتوليد حجج مستوى الجملة للموضوع والموقف والجانب.نحدد الكشف عن الجانب الوسيطة كطريقة ضرورية للسماح لهذا التحكم الدقيقة والتعبئة الجماعية بمجموعة بيانات مع 5،032 حجج مشروح مع جوانب.يوضح تقييمنا أن ARG-CTRL قادر على توليد حجج عالي الجودة وجزئي محددة، ينطبق على جيل الحجة المضادة التلقائي.نقوم بنشر الأوزان النموذجية وجميع مجموعات البيانات والرمز لتدريب ARG-CTRL.
We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we present the Arg-CTRL - a language model for argument generation that can be controlled to generate sentence-level arguments for a given topic, stance, and aspect. We define argument aspect detection as a necessary method to allow this fine-granular control and crowdsource a dataset with 5,032 arguments annotated with aspects. Our evaluation shows that the Arg-CTRL is able to generate high-quality, aspect-specific arguments, applicable to automatic counter-argument generation. We publish the model weights and all datasets and code to train the Arg-CTRL.
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