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Register a new userFrom Argument Search to Argumentative Dialogue: A Topic-independent Approach to Argument Acquisition for Dialogue Systems
من حجة البحث إلى الحوار الجدافي: نهج موضوع مستقل للحوادث الاستحواذ لنظم الحوار
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على الرغم من التقدم الرائع في مجال الوسائط الحسابية، غالبا ما تعتمد نظم الحوار المعنية ذات المهام الجدلية على المعرفة المهيكلة حول الحجج وعلاقاتها. نظرا لأن الاستحواذ اليدوي لهياكل الوسيطة هذه تستغرق وقتا طويلا، فإن النظم المقابلة غير مرنة فيما يتعلق بالموضوعات التي يمكنهم مناقشتها. لمعالجة هذه المشكلة، نقترح مجموعة من أنظمة الحوار الجدلية مع تكنولوجيا البحث الحجة التي تمكن النظام من مناقشة أي موضوع يقوم بمحرك البحث قادرا على العثور على حجج مناسبة. يستخدم نهجنا تصنيف العلاقة المستندة إلى التعلم في التعلم لتعيين الحجج المستردة في بنية شجرة عامة للاستخدام في أنظمة الحوار. نقيم النهج مع حالة من محرك البحث الفني ونموذج حوار تم إدخاله مؤخرا في دراسة مستخدم مكثفة فيما يتعلق بتماسك الحوار. تختلف النتائج بين الموضوعات التي تم التحقيق فيها (وبالتالي تعتمد على جودة البيانات الأساسية) ولكنها في بعض الحالات قريبة بشكل مدهش من النتائج التي تحققت مع هيكل الوسيطة المشروح يدويا.
Despite the remarkable progress in the field of computational argumentation, dialogue systems concerned with argumentative tasks often rely on structured knowledge about arguments and their relations. Since the manual acquisition of these argument structures is highly time-consuming, the corresponding systems are inflexible regarding the topics they can discuss. To address this issue, we propose a combination of argumentative dialogue systems with argument search technology that enables a system to discuss any topic on which the search engine is able to find suitable arguments. Our approach utilizes supervised learning-based relation classification to map the retrieved arguments into a general tree structure for use in dialogue systems. We evaluate the approach with a state of the art search engine and a recently introduced dialogue model in an extensive user study with respect to the dialogue coherence. The results vary between the investigated topics (and hence depend on the quality of the underlying data) but are in some instances surprisingly close to the results achieved with a manually annotated argument structure.
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