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Register a new userRestatement and Question Generation for Counsellor Chatbot
إعادة التقييم وتوليد السؤال للمستشار chatbot
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وسط ارتفاع احتياجات الصحة العقلية في المجتمع، يتم نشر العوامل الافتراضية بشكل متزايد في المشورة. من أجل تقديم المشورة ذات الصلة، يجب على المستشارين أولا الحصول على فهم للقضايا المطروحة من خلال إظهار التقاسم من المستشار. من المهم وبالتالي بالنسبة للمستشار Chatbot لتشجيع المستخدم على الانفتاح والتحدث. إحدى الطرق للحفاظ على تدفق المحادثة هي الاعتراف بالنقاط الرئيسية لاستشار المستشار عن طريق إعادة استخدامها، أو تحقيقها أكثر مع الأسئلة. تطبق هذه الورقة نماذج من مهام PLLP ذات الصلة ارتباطا وثيقا --- تلخيص وتوليد الأسئلة --- لإعادة التعميد وتوليد السؤال في سياق المشورة. أجرينا تجارب على مجموعة بيانات مشروحة يدويا من أزواج الكانتونية بعد الرد على الموضوعات المتعلقة بالوحدة والقلق الأكاديمي والقلق الاختبار. لقد حصلنا على أفضل أداء في كلا من إعادة تناوله وتوليد الأسئلة من خلال Bertsum Berting، وهو نموذج تلخيص حديثة، مع مجموعة البيانات اليدوية داخل المجال المعزز بمجموعة بيانات مفتوحة واسعة النطاق تلقائيا.
Amidst rising mental health needs in society, virtual agents are increasingly deployed in counselling. In order to give pertinent advice, counsellors must first gain an understanding of the issues at hand by eliciting sharing from the counsellee. It is thus important for the counsellor chatbot to encourage the user to open up and talk. One way to sustain the conversation flow is to acknowledge the counsellee's key points by restating them, or probing them further with questions. This paper applies models from two closely related NLP tasks --- summarization and question generation --- to restatement and question generation in the counselling context. We conducted experiments on a manually annotated dataset of Cantonese post-reply pairs on topics related to loneliness, academic anxiety and test anxiety. We obtained the best performance in both restatement and question generation by fine-tuning BertSum, a state-of-the-art summarization model, with the in-domain manual dataset augmented with a large-scale, automatically mined open-domain dataset.
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Despite excellent performance on tasks such as question answering, Transformer-based architectures remain sensitive to syntactic and contextual ambiguities. Question Paraphrasing (QP) offers a promising solution as a means to augment existing dataset
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