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Register a new userGenerating Negative Samples by Manipulating Golden Responses for Unsupervised Learning of a Response Evaluation Model
توليد العينات السلبية عن طريق معالجة الاستجابات الذهبية للتعلم غير المعدل لنموذج تقييم الاستجابة
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تقييم جودة الردود الناتجة عن أنظمة محادثة المجال المفتوحة هي مهمة صعبة. هذا جزئيا لأنه يمكن أن يكون هناك العديد من الردود المناسبة لتاريخ حوار معين. غالبا ما تفشل المقاييس المرجعية التي تعتمد على مقارنات إلى مجموعة من الاستجابات الصحيحة المعروفة في حساب هذا التنوع، وبالتالي ربط سيئة مع الحكم البشري. لمعالجة هذه المشكلة، قام الباحثون بالتحقيق في إمكانية تقييم جودة الاستجابة دون استخدام مجموعة من الردود الصحيحة المعروفة. أظهر روبر أنه يمكن إجراء نموذج تقييم الاستجابة التلقائي باستخدام التعلم غير المزعوم لمهمة التنبؤ بالكلام التالي (NUP). بالنسبة للتعلم غير المقترح لهذا النموذج، نقترح طريقة التلاعب بالاستجابة الذهبية لإنشاء استجابة سلبية جديدة تم تصميمها لتكون غير مناسب في السياق مع الحفاظ على التشابه العالي مع الاستجابة الذهبية الأصلية. نجد، من تجاربنا في مجموعات البيانات الإنجليزية، التي تستخدم العينات السلبية التي تم إنشاؤها بواسطة طريقتنا إلى جانب العينات السلبية العشوائية يمكن أن تزيد من ارتباط النموذج بالتقييمات البشرية. عملية توليد هذه العينات السلبية مؤتمتة ولا تعتمد على شرح الإنسان.
Evaluating the quality of responses generated by open-domain conversation systems is a challenging task. This is partly because there can be multiple appropriate responses to a given dialogue history. Reference-based metrics that rely on comparisons to a set of known correct responses often fail to account for this variety, and consequently correlate poorly with human judgment. To address this problem, researchers have investigated the possibility of assessing response quality without using a set of known correct responses. RUBER demonstrated that an automatic response evaluation model could be made using unsupervised learning for the next-utterance prediction (NUP) task. For the unsupervised learning of such model, we propose a method of manipulating a golden response to create a new negative response that is designed to be inappropriate within the context while maintaining high similarity with the original golden response. We find, from our experiments on English datasets, that using the negative samples generated by our method alongside random negative samples can increase the model's correlation with human evaluations. The process of generating such negative samples is automated and does not rely on human annotation.
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