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Register a new userUnderstanding and predicting user dissatisfaction in a neural generative chatbot
فهم وتنبؤ استياء المستخدم في chatbot اللثة العصبي
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أظهرت وكلاء الحوار القرملي العصبي القدرة المتزايدة على إجراء محادثات Chitchat قصيرة، عند تقييمها من قبل CrowDWorkers في الإعدادات التي تسيطر عليها. ومع ذلك، فإن أدائهم في نشر الحياة الحقيقية - التحدث إلى المستخدمين الدوافع الجوهرية في البيئات الصاخبة - أقل استكشافا جيدا. في هذه الورقة، نقوم بإجراء دراسة حالة مفصلة لنموذج توليدي عصبي منتشر كجزء من الكاردينال الغربي، جائزة اليكسا جائزة. نجد أن كلمات المستخدم غير واضحة هي مصدر رئيسي للأخطاء الإنتاجية مثل تجاهل، الهلوسة، إلغاء التكرار. ومع ذلك، حتى في سياقات لا لبس فيها، يقوم النموذج بشكل متكرر بإجراء أخطاء في التفكير. على الرغم من أن المستخدمين يعبرون عن عدم الرضا في الارتباط مع هذه الأخطاء، تعتمد أنواع عدم الاستياء بشكل معيادة (مثل إفرازات الاعتراضات والخصوصية) على عوامل إضافية - مثل المواقف الشخصية للمستخدم، والاستلاك غير المعتاد مسبق في المحادثة. أخيرا، نظهر أن كلمات المستخدم غير الراضية يمكن استخدامها كإشارة تعليمية شبه إشراف لتحسين نظام الحوار. نحن ندرب نموذجا للتنبؤ بعدم التشغيل التالي، وتظهر من خلال التقييم البشري كدالة ترتيب، فهو يختار كلام أعلى من الجودة الناتجة.
Neural generative dialogue agents have shown an increasing ability to hold short chitchat conversations, when evaluated by crowdworkers in controlled settings. However, their performance in real-life deployment -- talking to intrinsically-motivated users in noisy environments -- is less well-explored. In this paper, we perform a detailed case study of a neural generative model deployed as part of Chirpy Cardinal, an Alexa Prize socialbot. We find that unclear user utterances are a major source of generative errors such as ignoring, hallucination, unclearness and repetition. However, even in unambiguous contexts the model frequently makes reasoning errors. Though users express dissatisfaction in correlation with these errors, certain dissatisfaction types (such as offensiveness and privacy objections) depend on additional factors -- such as the user's personal attitudes, and prior unaddressed dissatisfaction in the conversation. Finally, we show that dissatisfied user utterances can be used as a semi-supervised learning signal to improve the dialogue system. We train a model to predict next-turn dissatisfaction, and show through human evaluation that as a ranking function, it selects higher-quality neural-generated utterances.
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