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Register a new userA Discourse-Aware Graph Neural Network for Emotion Recognition in Multi-Party Conversation
إدراك خطابي شبكة عصبية للتعرف على العاطفة في محادثة متعددة الأحزاب
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أصبح التعرف على العاطفة في محادثة متعددة الأحزاب (ermc) شعبية بشكل متزايد كقاعدة بحثية ناشئة في معالجة اللغة الطبيعية.يركز البحث المسبق على استكشاف معلومات متتابعة ولكن يتجاهل هياكل المحادثات.في هذه الورقة، يمكننا التحقيق في أهمية هياكل الخطاب في التعامل مع الإشارات السياقية الإعلامية والمعلومات الخاصة بالمتكلات الخاصة ب armc.تحقيقا لهذه الغاية، نقترح علما رسميا في رسم بياني (ERMC-DISGCN) ل ERMC.على وجه الخصوص، نقوم بتصميم الأزلاء العلائقية إلى رافعة تبعية المتكلم الذاتي للواقعاء نشر معلومات سياقية.علاوة على ذلك، فإننا نستنفذ عن مراقبة بوابات لاختيار إشارات أكثر إفادة ل armc من التحويلات المعالين.تظهر النتائج التجريبية طريقة أن أسلوبنا تتفوق على خطوط أساس متعددة، مما يوضح أن هياكل الخطاب ذات قيمة كبيرة ل armc.
Emotion recognition in multi-party conversation (ERMC) is becoming increasingly popular as an emerging research topic in natural language processing. Prior research focuses on exploring sequential information but ignores the discourse structures of conversations. In this paper, we investigate the importance of discourse structures in handling informative contextual cues and speaker-specific features for ERMC. To this end, we propose a discourse-aware graph neural network (ERMC-DisGCN) for ERMC. In particular, we design a relational convolution to lever the self-speaker dependency of interlocutors to propagate contextual information. Furthermore, we exploit a gated convolution to select more informative cues for ERMC from dependent utterances. The experimental results show our method outperforms multiple baselines, illustrating that discourse structures are of great value to ERMC.
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