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Register a new userA Bayesian Framework for Information-Theoretic Probing
إطار بايزي للحصول على معلومات نظرية
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pimentel et al. (2020) تم تحليلها مؤخرا التحقيق من منظور نظرية المعلومات. يجادلون بأن التحقيق يجب أن ينظر إليه على أنه يقترب المعلومات المتبادلة. هذا أدى إلى الاستنتاج دون المستوى إلى حد ما أن تمثل التمثيلات نفسها بالضبط نفس المعلومات حول المهمة المستهدفة كجمل أصلية. ومع ذلك، فإن المعلومات المتبادلة تفترض أن توزيع الاحتمالات الحقيقي لزوج من المتغيرات العشوائية معروفة، مما يؤدي إلى نتائج دون المستوى في الإعدادات التي لا يكون فيها. تقترح هذه الورقة إطارا جديدا لقياس ما نقوم بمصطلح معلومات البايز المتبادلة، والتي تحلل المعلومات من منظور عملاء البيئة --- السماح بنتائج أكثر بديهية في السيناريوهات مع البيانات المحدودة. على سبيل المثال، تحت Bayesian MI، لدينا أن البيانات يمكن أن تضيف معلومات، ومعالجة يمكن أن تساعد، والمعلومات يمكن أن تؤذي، مما يجعلها أكثر بديهية لتطبيقات التعلم الآلي. أخيرا، نطبق إطار عملنا على التحقيق حيث نعتقد أن المعلومات المتبادلة بايزي تعمل بشكل طبيعي بسهولة سهولة الاستخراج من خلال الحد الصريح لمعرفة الخلفية المتاحة لحل المهمة.
Pimentel et al. (2020) recently analysed probing from an information-theoretic perspective. They argue that probing should be seen as approximating a mutual information. This led to the rather unintuitive conclusion that representations encode exactly the same information about a target task as the original sentences. The mutual information, however, assumes the true probability distribution of a pair of random variables is known, leading to unintuitive results in settings where it is not. This paper proposes a new framework to measure what we term Bayesian mutual information, which analyses information from the perspective of Bayesian agents---allowing for more intuitive findings in scenarios with finite data. For instance, under Bayesian MI we have that data can add information, processing can help, and information can hurt, which makes it more intuitive for machine learning applications. Finally, we apply our framework to probing where we believe Bayesian mutual information naturally operationalises ease of extraction by explicitly limiting the available background knowledge to solve a task.
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