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Register a new userRetrieve, Discriminate and Rewrite: A Simple and Effective Framework for Obtaining Affective Response in Retrieval-Based Chatbots
استرداد وتمييز وإعادة كتابة: إطار بسيط وفعال للحصول على استجابة عاطفية في Chatbots المستندة إلى الاسترجاعات
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الحصول على الاستجابة العاطفية هي خطوة رئيسية في بناء نظم الحوار التعاطفية. تمت دراسة هذه المهمة كثيرا في Chatbots القائمة على الجيل، ولكن البحوث ذات الصلة في chatbots القائمة على الاسترجاع لا تزال في المرحلة المبكرة. تستند الأعمال الموجودة في Chatbots المستندة إلى الاسترجاع إلى إطار استرداد Rerank-Rerank، والتي لها مشكلة مشتركة تؤثر على التسمية على حساب جودة الاستجابة. لمعالجة هذه المشكلة، نقترح إطارا بسيطا وفعالا - إعادة الكتابة - إعادة الكتابة. يحل الإطار محل آلية إعادة النشر مع آلية جديدة للتمييز وإعادة الكتابة، والذي يتنبأ بتسمية التأثير على الاستجابة عالية الجودة المستردة من خلال وحدة التمييز ومواصلة إعادة كتابة الاستجابة غير الراضية عبر وحدة إعادة كتابة. هذا لا يمكن أن يضمن فقط جودة الاستجابة، ولكن أيضا تلبية علامة التأثير المحددة. بالإضافة إلى ذلك، فإن التحدي الآخر لهذا السطر البحث هو عدم وجود مجموعة بيانات استجابة عاطفية خارج الرف. لمعالجة هذه المشكلة واختبار إطار عملنا المقترح، سنلقيطر على كوربوس محادثة Douban Sentemental بناء على Corpus الأصلي Doubban Corpus. تظهر النتائج التجريبية أن إطار عملنا المقترح فعال وتفوق خطوط أساس تنافسية.
Obtaining affective response is a key step in building empathetic dialogue systems. This task has been studied a lot in generation-based chatbots, but the related research in retrieval-based chatbots is still in the early stage. Existing works in retrieval-based chatbots are based on Retrieve-and-Rerank framework, which have a common problem of satisfying affect label at the expense of response quality. To address this problem, we propose a simple and effective Retrieve-Discriminate-Rewrite framework. The framework replaces the reranking mechanism with a new discriminate-and-rewrite mechanism, which predicts the affect label of the retrieved high-quality response via discrimination module and further rewrites the affect unsatisfied response via rewriting module. This can not only guarantee the quality of the response, but also satisfy the given affect label. In addition, another challenge for this line of research is the lack of an off-the-shelf affective response dataset. To address this problem and test our proposed framework, we annotate a Sentimental Douban Conversation Corpus based on the original Douban Conversation Corpus. Experimental results show that our proposed framework is effective and outperforms competitive baselines.
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