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تعلم الإجابة الاستبيانة النفسية للكشف عن الشخصية
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تعتمد أبحاث الكشف عن الشخصية القائمة على النصوص الموجودة في الغالب على النهج التي يحركها البيانات لالتقاط إشارات شخصية ضمنيا في الوظائف عبر الإنترنت، تفتقر إلى إرشادات المعرفة النفسية. يلعب الاستبيان النفسي، الذي يحتوي على سلسلة من الأسئلة المخصصة المرتبطة بشدة إلى سمات الشخصية، دورا حاسما في تقييم شخصية التقرير الذاتي. نقول أن المشاركات التي أنشأها المستخدم تحتوي على محتويات مهمة يمكن أن تساعد في الإجابة على الأسئلة في استبيان، مما أدى إلى تقييم لشخصيته من خلال ربط النصوص والاستبيان. تحقيقا لهذه الغاية، نقترح نموذجا جديدا باسم شبكة الاستبيان النفسي المحسن (PQ-NET) لتوجيه اكتشاف الشخصية عن طريق تتبع المعلومات الهامة في النصوص مع استبيان. على وجه التحديد، يحتوي PQ-NET على جاريان: دفق سياق لتشفير كل جزء من النص في تمثيل نص سياقي، وتيار استبيان لالتقاط المعلومات ذات الصلة في تمثيل النص السياقي لإنشاء تمثيلات إجابات محتملة للاستبيان. يتم استخدام تمثيل الإجابات المحتملة لتعزيز تمثيل النص السياقي وفائدة التنبؤ بالشخصية. تظهر النتائج التجريبية على مجموعة بيانات اثنين من تفوق PQ-NET في التقاط إشارات مفيدة من المشاركات للكشف عن الشخصية.
Existing text-based personality detection research mostly relies on data-driven approaches to implicitly capture personality cues in online posts, lacking the guidance of psychological knowledge. Psychological questionnaire, which contains a series of dedicated questions highly related to personality traits, plays a critical role in self-report personality assessment. We argue that the posts created by a user contain critical contents that could help answer the questions in a questionnaire, resulting in an assessment of his personality by linking the texts and the questionnaire. To this end, we propose a new model named Psychological Questionnaire enhanced Network (PQ-Net) to guide personality detection by tracking critical information in texts with a questionnaire. Specifically, PQ-Net contains two streams: a context stream to encode each piece of text into a contextual text representation, and a questionnaire stream to capture relevant information in the contextual text representation to generate potential answer representations for a questionnaire. The potential answer representations are used to enhance the contextual text representation and to benefit personality prediction. Experimental results on two datasets demonstrate the superiority of PQ-Net in capturing useful cues from the posts for personality detection.
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https://aclanthology.org/
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