[PDF] Top 20 Multi-Task Learning for Classification with Dirichlet Process Priors
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Multi-Task Learning for Classification with Dirichlet Process Priors
... a task-clustering algorithm with K-Nearest ...“high-level” task characteristics, other than the features used for learning the model parameters of individual tasks, are needed to decide the relative ... See full document
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Multi Task Learning of Keyphrase Boundary Classification
... boundary classification (KBC) is the task of detecting keyphrases in sci- entific articles and labelling them with re- spect to predefined ...this task is so far un- derexplored, partly due to the ... See full document
6
Keeping Consistency of Sentence Generation and Document Classification with Multi Task Learning
... The automated generation of information in- dicating the characteristics of articles such as headlines, key phrases, summaries and categories helps writers to alleviate their work- load. Previous research has tackled ... See full document
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Concept Classification with Bayesian Multi task Learning
... to multi-task learning is the convergence of models obtained from different ...the multi-task setting, even for weak coupling ...improve classification perfor- ...to ... See full document
8
Deep Multi Task Learning with Shared Memory for Text Classification
... 5.5 Multi-task Learning of Product Reviews Table 4 shows the classification accuracies on the tasks of product ...“Single Task” shows the results of the baseline for each individ- ual ... See full document
10
Adversarial Multi task Learning for Text Classification
... for multi-task learning, which focus on learning the shared layers to extract the common and task-invariant ...by task-specific features or the noise brought by other ... See full document
10
Multi Task Learning of Pairwise Sequence Classification Tasks over Disparate Label Spaces
... a multi-task learning archi- tecture that (i) leverages potential synergies be- tween classifier functions relating shared represen- tations with disparate label spaces and (ii) enables ... See full document
11
Extractive Summarization Using Multi Task Learning with Document Classification
... tion task, it is more difficult to train from the last dataset than the first dataset because information related to document subjects are more truncated than the reference ... See full document
10
Twitter Demographic Classification Using Deep Multi modal Multi task Learning
... on task-specific annotated (mechanical turk) data or data collected based on different phrase indicators from user’s tweet or de- scription that was not a part of training ... See full document
6
Multi task learning for interpretable cause of death classification using key phrase prediction
... CoD classification, the prediction layer out- puts the probabilities over the 18 CoD categories, and we choose the one with the highest probabil- ...CoD classification and mean squared error for key phrase ... See full document
6
Classification with Incomplete Data Using Dirichlet Process Priors
... the classification boundaries to be similar if two tasks are deemed to be ...the task-level (Xue et al. 2007 employs task-level ...each task, we can find the task-specific probability ... See full document
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Gated Multi Task Network for Text Classification
... of multi-task learning and neural networks has shown its advantages in many tasks, ranging from computer vision (Misra et ...2008). Multi-task learn- ing (MTL) has the ability to share ... See full document
6
Learning representations for sentiment classification using Multi task framework
... transfer learning and multi-task learning approaches to transfer knowl- edge across different datasets and ...for multi- task learning have become very popular, ranging ... See full document
10
MTNA: A Neural Multi task Model for Aspect Category Classification and Aspect Term Extraction On Restaurant Reviews
... a multi-task learning system using deep learning methods for various natural language processing ...NER task, the pre- dictions of this model depend only on the infor- mation of the ... See full document
6
Locale agnostic Universal Domain Classification Model in Spoken Language Understanding
... domain classification model accuracy in Spo- ken Language Understanding and user expe- rience even if new locales do not have suf- ficient data and 2) reduce the cost of scaling the domain classifier to a large ... See full document
7
An Interactive Multi Task Learning Network for End to End Aspect Based Sentiment Analysis
... is multi-task learning, where one conventional framework is to employ a shared network and two task-specific network to derive a shared feature space and two task-specific feature ... See full document
12
Enhanced multi task compressive sensing using Laplace priors and MDL based task classification
... The second part of this work comes from the following observation. Specifically, in order to provide satisfactory signal reconstruction performance, the MCS technique from [7], together with the newly proposed LMCS ... See full document
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Representation Learning Using Multi Task Deep Neural Networks for Semantic Classification and Information Retrieval
... between multi-task learning and neu- ral nets is quite natural; the general idea dates back to (Caruana, ...as classification and rank- ing. Further, considering that multi-task ... See full document
10
Multi Task Label Embedding for Text Classification
... Multi-task learning in text classification lever- ages implicit correlations among related tasks to extract common features and yield perfor- mance ...each task as in- dependent and ... See full document
9
All in one : multi task learning for rumour verification
... stance classification and rumour detection tasks (discussed in Section 2), we are following the branchLSTM approach described in Zubiaga et ...stance classification task is annotated at the tweet ... See full document
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