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Register a new userConstructing Emotional Consensus and Utilizing Unpaired Data for Empathetic Dialogue Generation
بناء إجماع عاطفي واستخدام البيانات غير المستهلكة لتوليد الحوار المتعاطفة
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تهدف الأبحاث حول الحوار التعاطف إلى إنهاء وكيل بسعة الفهم الدقيق والاستجابة السليمة للعواطف. تركز النماذج الحالية لتوليد الحوار المتعاطفة على تدفق المشاعر في اتجاه واحد، أي من السياق للاستجابة. نقول أن إجراء محادثة متعاطفة هي عملية ثنائية الاتجاه، حيث يحدث التعاطف عندما يمكن أن تتقارب عواطف اثنين من المحاورين في نفس النقطة، أي، الوصول إلى توافق عاطفي. علاوة على ذلك، نجد أيضا أن الحوار المتعاطفة Corpus محدودة للغاية، مما يؤدي إلى تقييد الأداء النموذجي. لمعالجة المشكلات المذكورة أعلاه، نقترح نموذجا ثنائيا، ثنائي إلكتروني، لإنشاء إجماع عاطفي في وقت واحد واستخدام بعض البيانات الخارجية غير المستهلكة. على وجه التحديد، يدمج نموذجنا نموذج حوار إلى الأمام، نموذج حوار للخلف، ومتغير كامن منفصل يمثل الإجماع العاطفي في هندسة موحدة. ثم، لتخفيف قيود البيانات المقترنة، استخراج البيانات العاطفية غير المستفادة من محادثات مفتوحة واستخدامها ثنائي إفريقيا لإنتاج العينات الزائفة القابلة للتعاطف الزائفة، وهي أكثر كفاءة وتكلفة منخفضة من الشرح البشري. توضح التقييمات التلقائية والإنسانية أن أسلوبنا تتفوق على خطوط أساس تنافسية في إنتاج ردود متماسكة ومواءة.
Researches on dialogue empathy aim to endow an agent with the capacity of accurate understanding and proper responding for emotions. Existing models for empathetic dialogue generation focus on the emotion flow in one direction, that is, from the context to response. We argue that conducting an empathetic conversation is a bidirectional process, where empathy occurs when the emotions of two interlocutors could converge on the same point, i.e., reaching an emotional consensus. Besides, we also find that the empathetic dialogue corpus is extremely limited, which further restricts the model performance. To address the above issues, we propose a dual-generative model, Dual-Emp, to simultaneously construct the emotional consensus and utilize some external unpaired data. Specifically, our model integrates a forward dialogue model, a backward dialogue model, and a discrete latent variable representing the emotional consensus into a unified architecture. Then, to alleviate the constraint of paired data, we extract unpaired emotional data from open-domain conversations and employ Dual-Emp to produce pseudo paired empathetic samples, which is more efficient and low-cost than the human annotation. Automatic and human evaluations demonstrate that our method outperforms competitive baselines in producing coherent and empathetic responses.
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