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Register a new userLearning Task Sampling Policy for Multitask Learning
سياسة أخذ عينات مهمة التعلم للتعلم المتعدد
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لقد ثبت أن التدريبات متعددة المهام مع المهام الإضافية يمكن أن تحسن جودة المهمة المستهدفة من خلال نقل المهام العابر.ومع ذلك، من المحتمل أن تكون أهمية كل مهمة مساعدة للمهمة الأساسية غير معروفة مسبقا.في حين أن أهمية الأثقال ذات المهام الإضافية يمكن ضبطها يدويا، إلا أنها تصبح عمليا غير قابلة للتنفيذ مع عدد المهام.لمعالجة هذا، نقترح طريقة بحث تقوم تلقائيا بتعيين الأوزان الأهمية.نقوم بصياغة ذلك كمشكلة تعليمية للتعزيز وتعلم جدول أخذ عينات من المهام بناء على دقة تقييم النموذج متعدد المهام.يوضح تقييمنا التجريبي على XNLI والغراء أن أسلوبنا تتفوق على أخذ العينات الموحدة والساعي الأساسي المهمة الموحدة المقابلة.
It has been shown that training multi-task models with auxiliary tasks can improve the target task quality through cross-task transfer. However, the importance of each auxiliary task to the primary task is likely not known a priori. While the importance weights of auxiliary tasks can be manually tuned, it becomes practically infeasible with the number of tasks scaling up. To address this, we propose a search method that automatically assigns importance weights. We formulate it as a reinforcement learning problem and learn a task sampling schedule based on the evaluation accuracy of the multi-task model. Our empirical evaluation on XNLI and GLUE shows that our method outperforms uniform sampling and the corresponding single-task baseline.
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We present our entry into the 2021 3C Shared Task Citation Context Classification based on Purpose competition. The goal of the competition is to classify a citation in a scientific article based on its purpose. This task is important because it coul
d potentially lead to more comprehensive ways of summarizing the purpose and uses of scientific articles, but it is also difficult, mainly due to the limited amount of available training data in which the purposes of each citation have been hand-labeled, along with the subjectivity of these labels. Our entry in the competition is a multi-task model that combines multiple modules designed to handle the problem from different perspectives, including hand-generated linguistic features, TF-IDF features, and an LSTM-with- attention model. We also provide an ablation study and feature analysis whose insights could lead to future work.
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Darknet market forums are frequently used to exchange illegal goods and services between parties who use encryption to conceal their identities. The Tor network is used to host these markets, which guarantees additional anonymization from IP and loca
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Deep reinforcement learning has shown great potential in training dialogue policies. However, its favorable performance comes at the cost of many rounds of interaction. Most of the existing dialogue policy methods rely on a single learning system, wh
ile the human brain has two specialized learning and memory systems, supporting to find good solutions without requiring copious examples. Inspired by the human brain, this paper proposes a novel complementary policy learning (CPL) framework, which exploits the complementary advantages of the episodic memory (EM) policy and the deep Q-network (DQN) policy to achieve fast and effective dialogue policy learning. In order to coordinate between the two policies, we proposed a confidence controller to control the complementary time according to their relative efficacy at different stages. Furthermore, memory connectivity and time pruning are proposed to guarantee the flexible and adaptive generalization of the EM policy in dialog tasks. Experimental results on three dialogue datasets show that our method significantly outperforms existing methods relying on a single learning system.
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