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Register a new userFew-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks
عدد قليل من العاطفة التعرف على المحادثة مع شبكات النماذج النموذجية المتسلسلة
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تم إجراء عدة دراسات حديثة حول التفاعلات البشرية الدينية على المحادثات دون أهداف تجارية محددة. ومع ذلك، قد تستفيد العديد من الشركات من الدراسات المخصصة لبيئات أكثر دقة مثل خدمات ما بعد البيع أو استطلاعات رضا العملاء. في هذا العمل، نضع أنفسنا في نطاق خدمة عملاء الدردشة الحية التي نريد اكتشاف العواطف وتطورها في تدفق المحادثة. يؤدي هذا السياق إلى تحديات متعددة تتراوح من استغلال مجموعات البيانات المحظورة والصغيرة والغلبية غير المستمرة لإيجاد وتكيف مع طرق هذا السياق. نحن نتعامل مع هذه التحديات باستخدام عدد قليل من التعلم أثناء صنع الفرضية التي يمكن أن تخدم تصنيف المشاعر المحادثة لغات مختلفة وتسميات متفرق. نحن نساهم باقتراح تباين من الشبكات النموذجية للحصول على تسلسل وضع العلامات في المحادثة التي نسمينا protoseq. نحن نختبر هذه الطريقة على رقمين مع لغات مختلفة: المحادثات اليومية في محادثات الدردشة الإنجليزية وخدمة العملاء في الفرنسية. عند تطبيقها على تصنيف العاطفة في المحادثات، أثبتت طريقنا أنها تنافسية حتى مقارنة بأخرى أخرى.
Several recent studies on dyadic human-human interactions have been done on conversations without specific business objectives. However, many companies might benefit from studies dedicated to more precise environments such as after sales services or customer satisfaction surveys. In this work, we place ourselves in the scope of a live chat customer service in which we want to detect emotions and their evolution in the conversation flow. This context leads to multiple challenges that range from exploiting restricted, small and mostly unlabeled datasets to finding and adapting methods for such context. We tackle these challenges by using Few-Shot Learning while making the hypothesis it can serve conversational emotion classification for different languages and sparse labels. We contribute by proposing a variation of Prototypical Networks for sequence labeling in conversation that we name ProtoSeq. We test this method on two datasets with different languages: daily conversations in English and customer service chat conversations in French. When applied to emotion classification in conversations, our method proved to be competitive even when compared to other ones.
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Emotion recognition in conversation has received considerable attention recently because of its practical industrial applications. Existing methods tend to overlook the immediate mutual interaction between different speakers in the speaker-utterance
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