task learning
Identifying beneficial task relations for multi task learning in deep neural networks
... multi-task learning in the ...main task, for instance through a non-uniform drawing of the task con- sidered at each training iteration, or through an adaptation of the learning ...
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When does deep multi task learning work for loosely related document classification tasks?
... multi-task learning architec- tures consist of a multi-layered perceptron with two hidden ...multi-task learning, those layers are shared across all ...the task-specific parameters, ...
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SC LSTM: Learning Task Specific Representations in Multi Task Learning for Sequence Labeling
... Multi-task learning (MTL) has been studied recently for sequence ...target task. Jointly learning multiple tasks in a way that benefit all of them simultaneously can in- crease the utility of ...
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Multi Task Learning for Coherence Modeling
... In contrast to existing approaches, we propose a more generalized framework that allows neural models to encode information about the types of grammatical roles all words in a sentence partic- ipate in, rather than ...
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All in one : multi task learning for rumour verification
... multi- task learning ...of task-specific layers. The possible task combinations are shown as dotted lines on Figure 3 that can be present or absent depending on the ...(3) learning all ...
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AutoSeM: Automatic Task Selection and Mixing in Multi Task Learning
... Multi-task learning (MTL) has achieved suc- cess over a wide range of problems, where the goal is to improve the performance of a primary task using a set of relevant auxiliary ...primary task ...
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Deep Automated Multi task Learning
... Multi-task learning (MTL) has recently contributed to learning better representa- tions in service of various NLP ...primary task, by jointly training on a secondary ...primary task in ...
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Multi-task Learning and Catastrophic Forgetting in Continual Reinforcement Learning
... sequential learning, whenever a trained model, upon training in a new task, moves abruptly in the space of parameters, effectively “forgetting” the original ...second task, these algorithms add an ...
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Multi task Learning for Natural Language Generation in Task Oriented Dialogue
... In task-oriented dialogues, Natural Language Generation (NLG) is the final and crucial step to produce user-facing system ...multi-task learning framework, NLG-LM, for natural language ...the ...
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EXO OLO TASK Learning Model: What Should Students do in the Class?
... teacher-centered learning practices in the classroom, minimal dialogue and interaction, collaboration and less challenging (Nofrion, ...of learning goals including Geography subjects as one of the Key ...
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Bounds for Linear Multi-Task Learning
... multi-task learning, but a trivial consequence of the fact that we estimate an average of m probabilities (in contrast to Ben David, 2003, where bounds are valid for each individual task - of course ...
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Grounding Language for Interactive Task Learning
... for learning words that are grounded in the agent's physical environment and actions with a table-top robotic ...to task learning, but their lan- guage comprehension systems are ...
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Few Shot and Zero Shot Learning for Historical Text Normalization
... • Prediction layer: a final feed-forward layer that linearly transforms the decoder output and performs a softmax to predict a distribu- tion over all possible output characters. Hyperparameters We tuned our hyperparame- ...
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Generalization of auditory sensory and cognitive learning in typically developing children
... of learning following non-linguistic audi- tory training to measures of language, including reading, speech perception and phonological awareness ...on-task learning, but no generalization to ...
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Learning Multi-Task Communication with Message Passing for Sequence Learning
... multi-task learning methods have also been proposed to model the relationships between ...complex learning strategies and introduce a priori information between different tasks, which are usually not ...
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Latent Multi-Task Architecture Learning
... for learning which parts of multi-task models to share, with a small set of additional parameters to learn, can achieve significant and consistent improvements over strong baseline ...
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DUT NLP at MEDIQA 2019: An Adversarial Multi Task Network to Jointly Model Recognizing Question Entailment and Question Answering
... representation learning and inter-sentence relationship modeling, which allows knowledge transfer from other ...QA task for the multi-task learning to ensure the consistency between RQE and QA ...
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Multi Task Networks with Universe, Group, and Task Feature Learning
... that task structure is usually un- clear, Evgeniou and Pontil (2004) extended sup- port vector machines for single-task learning in a multi-task scenario by penalizing models if they are too ...
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Investigating Meta Learning Algorithms for Low Resource Natural Language Understanding Tasks
... multi-task learning to representation learning (Liu et ...multi-task learning, Liu et al. (2019) improve the BERT model with multi-task learning and their proposed MT-DNN ...
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A review on multi-task metric learning
... Person re-identification over camera networks Ma et al. [43] uses their proposed multi-task maximally collapsing metric learning to solve the person re-identification over camera networks. Person ...
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