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Register a new userPerspective-taking and Pragmatics for Generating Empathetic Responses Focused on Emotion Causes
منظور الاستيلاء والبراغماتية لتوليد الاستجابات التعاطفية التي تركز على أسباب العاطفة
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التعاطف هو قدرات معرفية معقدة تستند إلى منطق الدول العاطفية الأخرى. من أجل فهم الآخرين بشكل أفضل والتعبير عن التعاطف الأقوى في الحوارات، نجادل بأننا يجب معالجة قضيتين في الوقت نفسه: (1) تحديد أي كلمة هي سبب عاطفة الآخر من كلامه و (2) تعكس تلك كلمات محددة في توليد الاستجابة. ومع ذلك، فإن النهج السابقة للتعرف على العاطفة تسبب الكلمات في النص تتطلب شرحا مستوى الكلامة الفرعية، والتي يمكن أن تكون مطالبة. من خلال الإلهام من الإدراك الاجتماعي، فإننا نستفيد من مقدر إجمالي لاستنتاج العاطفة تسبب كلمات من كلام مع عدم وجود تسمية على مستوى الكلمات. أيضا، نقدم طريقة جديدة بناء على البراغماتية لجعل نماذج الحوار تركز على الكلمات المستهدفة في المدخلات أثناء التوليد. تنطبق طريقنا على أي نماذج حوار بدون تدريب إضافي على الطيران. نظهر أن نهجنا يحسن العديد من وكلاء حوار أفضل أداء في توليد الاستجابات التعاطفية الأكثر تركيزا من حيث التقييم التلقائي والبشري.
Empathy is a complex cognitive ability based on the reasoning of others' affective states. In order to better understand others and express stronger empathy in dialogues, we argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other's emotion from his or her utterance and (ii) reflecting those specific words in the response generation. However, previous approaches for recognizing emotion cause words in text require sub-utterance level annotations, which can be demanding. Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label. Also, we introduce a novel method based on pragmatics to make dialogue models focus on targeted words in the input during generation. Our method is applicable to any dialogue models with no additional training on the fly. We show our approach improves multiple best-performing dialogue agents on generating more focused empathetic responses in terms of both automatic and human evaluation.
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Current approaches to empathetic response generation focus on learning a model to predict an emotion label and generate a response based on this label and have achieved promising results. However, the emotion cause, an essential factor for empathetic
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