Multi-task Loss and Network Training
Multi view and multi task training of RST discourse parsers
... In this work we consider time and factuality auxiliary tasks in a multi-task setup. We use two resources for this, Factbank and Timebank, described next. We also include information concerning co-reference ...
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Transductive Auxiliary Task Self Training for Neural Multi Task Models
... auxiliary task self- training, a straightforward way to improve the per- formance of multi-task ...main task test data with auxiliary task labels which we subse- quently included ...
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Gated Multi Task Network for Text Classification
... ral Network (CNN) has shown great success in many Natural Language Processing (NLP) ...the network would be confused by the helpless even harmful fea- tures, generating undesired interference be- tween ...
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Weighted and Multi-Task Loss for Rare Audio Event Detection
... tailored loss functions, namely weighted loss and multi-task loss to tackle the well-known issues of rare audio event detection ...weighted loss can be used to explicitly weight ...
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Multi Task Learning for Improved Discriminative Training in SMT
... of multi-task learning depends on data structured along tasks that exhibit a proper balance of shared and individual knowl- edge, or whether its inherent feature selection and regularization makes ...
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Training Complex Models with Multi-Task Weak Supervision
... single-task setting (Ratner et al. 2016; 2018), and gener- ally considered conditionally-independent sources (Anand- kumar et al. 2014; Dawid and Skene 1979), we demonstrate that our multi-task aware ...
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Multi-task Adversarial Network for Disentangled Feature Learning
... the multi-task and the adversarial task model both leverage the font and glyph la- bel supervision, the disentangled feature obtained from ad- versarial training performs much better than that ...
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MCapsNet: Capsule Network for Text with Multi Task Learning
... single-task network enhanced by capsules is already a strong ...with multi- ple kernel sizes further improves the performance and get best accuracy on 4 ...capsule network outperforms conven- ...
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Anonymous Mission Network for Task-oriented ESP Training
... mission training to mutually improve professional skills in English, who are majoring in business, tourism and fashion ...Mission Network, but also a teaching for the English teachers in the ...
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HyperAdam: A Learnable Task-Adaptive Adam for Network Training
... optimize network parameters has emerged as a promising research ...a network for training, its parameter update in each iteration generated by HyperAdam is an adaptive combination of multiple updates ...
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Emo2Vec: Learning Generalized Emotion Representation by Multi task Training
... by multi-task learning six dif- ferent emotion-related tasks, including emo- tion/sentiment analysis, sarcasm classification, stress detection, abusive language classifica- tion, insult detection, and ...
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Learning What to Share: Leaky Multi Task Network for Text Classification
... 3 Network and Information Center, Shanghai Jiao Tong University { jinyh } ...Neural network based multi-task learning has achieved great success on many NLP problems, which focuses on sharing ...
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Automatic Multi-task Learning System for Abnormal Network Traffic Detection
... abnormal network traf- ...novel multi-task learning system based on convolutional neural network, which can simultaneously solve the tasks of malware detection, VPN-capsulation recogni- tion ...
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A neural network multi-task learning approach to biomedical named entity recognition
... used multi-task deep neu- ral networks to learn representations for information retrieval and semantic classification by jointly training a model for both tasks which has shared and private lay- ...
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SNR: Sub-Network Routing for Flexible Parameter Sharing in Multi-Task Learning
... in multi-task learn- ing, the classic SB model is known to suffer from signifi- cant degeneration in accuracy when tasks are unrelated to each other (Ma et ...try multi-task models as well as ...
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Multi-Stage Multi-Task Feature Learning
... for multi-task learning settings (involving different loss functions and non-convex regularization terms) using multi-stage ...non-convex multi-task feature learning problem and ...
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A Multi-Task Convolutional Neural Network for Renal Tumor Segmentation and Classification Using Multi-Phasic CT Images
... neural network to the segmentation of renal ...and multi-phasic CT images can be ...neural network with larger receptive field would be better on learning features from objects of different scales in ...
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An Interactive Multi Task Learning Network for End to End Aspect Based Sentiment Analysis
... This task is usually done in a pipeline man- ner, with aspect term extraction performed first, followed by sentiment predictions to- ward the extracted aspect ...of training information that might be ...
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Table Filling Multi Task Recurrent Neural Network for Joint Entity and Relation Extraction
... Filling Multi-Task Recurrent Neural Network (TF-MTRNN) model that reduces the entity recognition and relation classifica- tion tasks to a table-filling problem and models their ...neural ...
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A Multi Attention based Neural Network with External Knowledge for Story Ending Predicting Task
... Two popular forms of evaluation tasks exist in this field: cloze-style query and text-span matching. Cloze-style query, such as SQuAD published by Stanford University, focuses on predicting existing text from the ...
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