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Register a new userPast, Present, and Future: Conversational Emotion Recognition through Structural Modeling of Psychological Knowledge
الماضي، الحاضر، والمستقبل: عاطفة المحادثة الاعتراف من خلال النمذجة الهيكلية للمعرفة النفسية
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تعتبر التعرف على عاطلة المحادثة (CER) مهمة للتنبؤ بمشاعر الكلام في سياق محادثة. على الرغم من أن نمذجة سياق المحادثة والتفاعلات بين المتحدثين قد درست على نطاق واسع، إلا أنه من المهم النظر في الدولة النفسية للمتحدث، والتي تسيطر على عمل ومكبر الصوت. تقدم الطريقة التي من بين الفنون المعرفة المنطقية (CSK) نموذجا نفسيا بطريقة متتالية (إلى الأمام والخلف). ومع ذلك، فإنه يتجاهل التفاعلات النفسية الهيكلية بين الكلام. في هذه الورقة، نقترح رسم بياني تفاعل علمي المعرفة (Skaig). في الرسم البياني المرتبط محليا، سيتم تعزيز النطق المستهدف مع معلومات العمل التي استنتجها من السياق الماضي ونهايها الضمنية السياق المستقبلية. الكلام مرتبط بالنظر في الاعتبار التأثير الحالي من نفسه. علاوة على ذلك، نستخدم CSK لإثراء الحواف بتمثيل المعرفة وعمل Skaig مع محول الرسم البياني. تقوم طريقةنا بتحقيق الأداء الحكومي والتنافسي في أربعة مجموعات بيانات CRES.
Conversational Emotion Recognition (CER) is a task to predict the emotion of an utterance in the context of a conversation. Although modeling the conversational context and interactions between speakers has been studied broadly, it is important to consider the speaker's psychological state, which controls the action and intention of the speaker. The state-of-the-art method introduces CommonSense Knowledge (CSK) to model psychological states in a sequential way (forwards and backwards). However, it ignores the structural psychological interactions between utterances. In this paper, we propose a pSychological-Knowledge-Aware Interaction Graph (SKAIG). In the locally connected graph, the targeted utterance will be enhanced with the information of action inferred from the past context and intention implied by the future context. The utterance is self-connected to consider the present effect from itself. Furthermore, we utilize CSK to enrich edges with knowledge representations and process the SKAIG with a graph transformer. Our method achieves state-of-the-art and competitive performance on four popular CER datasets.
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