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Register a new userEmotion Inference in Multi-Turn Conversations with Addressee-Aware Module and Ensemble Strategy
الاستدلال العاطفة في محادثات متعددة الدورات مع الوحدة النمطية المفيدة واستراتيجية الفرقة
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تهدف الاستدلال العاطفة في المحادثات متعددة الدورات إلى التنبؤ بمشاعر المشارك في الدور التالي المقبل دون معرفة استجابة المشارك بعد، وهي خطوة ضرورية للتطبيقات مثل تخطيط الحوار. ومع ذلك، فإن التحدي الشديد لإدراك وسبب مشاعر المشاركين في المستقبل، بسبب عدم وجود معلومات عن المستقبل من المستقبل. علاوة على ذلك، فمن الضروري استنتاج المشاعر لالتقاط خصائص الانتشار العاطفي في المحادثات، مثل الثبات والمعاجين. في هذه الدراسة، نركز على التحقيق في مهمة استنتاج المشاعر في محادثات متعددة الدورات من خلال نمذجة انتشار الدول العاطفية بين المشاركين في تاريخ المحادثة، واقتراح وحدة نمط تدرك المرسل إليه تلقائيا ما إذا كان المشارك يحتفظ الحالة العاطفية التاريخية أو تتأثر بالآخرين في المنعطف القادم المقبل. بالإضافة إلى ذلك، نقترح استراتيجية فرقة لتعزيز الأداء النموذجي. تظهر الدراسات التجريبية على ثلاث مجموعات محادثة محادثة مختلفة مختلفة فعالية النموذج المقترح على العديد من خطوط الأساس القوية.
Emotion inference in multi-turn conversations aims to predict the participant's emotion in the next upcoming turn without knowing the participant's response yet, and is a necessary step for applications such as dialogue planning. However, it is a severe challenge to perceive and reason about the future feelings of participants, due to the lack of utterance information from the future. Moreover, it is crucial for emotion inference to capture the characteristics of emotional propagation in conversations, such as persistence and contagiousness. In this study, we focus on investigating the task of emotion inference in multi-turn conversations by modeling the propagation of emotional states among participants in the conversation history, and propose an addressee-aware module to automatically learn whether the participant keeps the historical emotional state or is affected by others in the next upcoming turn. In addition, we propose an ensemble strategy to further enhance the model performance. Empirical studies on three different benchmark conversation datasets demonstrate the effectiveness of the proposed model over several strong baselines.
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