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Register a new userEnd-to-End Conversational Search for Online Shopping with Utterance Transfer
البحث عن محادثة نهاية إلى نهاية للتسوق عبر الإنترنت مع نقل الكلام
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يمكن أن أنظمة البحث عن المحادثة الناجحة تجربة تسوق طبيعية وتكيفية وتفاعلية لعملاء التسوق عبر الإنترنت. ومع ذلك، فإن بناء هذه الأنظمة من الصفر تواجه تحديات الكلمة الحقيقية من كل من مخطط المنتج / المعرفة غير الصحيحة ونقص بيانات حوار التدريب. في هذا العمل، نقترح أولا Convechearch، ونظام بحث محادثة نهاية إلى نهاية يجمع عميقا من نظام الحوار مع البحث. إنه يرفع ملف تعريف النص لاسترداد المنتجات، وهو أكثر قوة ضد مخطط / معرفة المنتج غير الكاملة مقارنة باستخدام سمات المنتج وحدها. ثم نتطلع إلى عدم وجود تحديات البيانات من خلال اقتراح نهج نقل الكلام الذي يولد كلام الحوار باستخدام مربع الحوار الحالي من المجالات الأخرى، والاستفادة من بيانات سلوك البحث من تجارة التجزئة الإلكترونية. مع نقل الكلام، نقدم مجموعة بيانات جديدة للبحث عن محادثة للتسوق عبر الإنترنت. تبين التجارب أن طريقة نقل الكلام لدينا يمكن أن تحسن بشكل كبير من توفر بيانات الحوار التدريبية دون تحديد مصادر الحشد، وتفوق نظام البحث عن المحادثة بشكل كبير على أفضل خط الأساس اختباره.
Successful conversational search systems can present natural, adaptive and interactive shopping experience for online shopping customers. However, building such systems from scratch faces real word challenges from both imperfect product schema/knowledge and lack of training dialog data. In this work we first propose ConvSearch, an end-to-end conversational search system that deeply combines the dialog system with search. It leverages the text profile to retrieve products, which is more robust against imperfect product schema/knowledge compared with using product attributes alone. We then address the lack of data challenges by proposing an utterance transfer approach that generates dialogue utterances by using existing dialog from other domains, and leveraging the search behavior data from e-commerce retailer. With utterance transfer, we introduce a new conversational search dataset for online shopping. Experiments show that our utterance transfer method can significantly improve the availability of training dialogue data without crowd-sourcing, and the conversational search system significantly outperformed the best tested baseline.
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