[PDF] Top 20 Adversarial Multi task Learning for Text Classification
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Adversarial Multi task Learning for Text Classification
... Figure 5 illustrates this phenomenon. Here, we randomly sample a sentence from the validation set of Baby task and analyze the changes of the predicted sentiment score at different time steps, which are obtained ... See full document
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Extractive Summarization Using Multi Task Learning with Document Classification
... For the NIKKEI financial report dataset, we used LEAD, which extracts the leading three sentences of a document as a baseline. We also built a base- line classifier LREG using logistic regression and human engineered ... See full document
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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
Twitter Demographic Classification Using Deep Multi modal Multi task Learning
... this task. Our classifier uses a deep multi-modal multi- task learning architecture to reach a state- of-the-art performance, achieving an F1- score of ... See full document
6
Adversarial Reprogramming of Text Classification Neural Networks
... original task on which the network was trained, we also present re- sults of white-box adversarial reprogramming on untrained random ...on adversarial re- programming of untrained ImageNet models ... See full document
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Exploring Human-Like Reading Strategy for Abstractive Text Summarization
... abstractive text summariza- tion, which however is able to improve the effectiveness of the summarization by considering the process of reading com- prehension and logical ...Hybrid learning model for ... See full document
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MCapsNet: Capsule Network for Text with Multi Task Learning
... for multi-task learn- ing (McapsNet), which is unified, simple, effec- tive and can cluster the feature for ...a Task Routing algorithm to route the feature flows to tasks and vote for the classes, ... See full document
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Perceptual Pyramid Adversarial Networks for Text-to-Image Synthesis
... Pyramid Adversarial Net- work (PPAN) for text-to-image synthesis task, by directly generating multi-scale images conditioned on texts in an adversarial ...regularize multi- scale ... See full document
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Multi task learning for historical text normalization: Size matters
... Our main observation—that the size of the tar- get dataset is most predictive of multi-task learning gains—runs counter previous findings for other NLP tasks (Martínez Alonso and Plank, 2017; Bin- ... See full document
6
HotFlip: White Box Adversarial Examples for Text Classification
... machine learning model that are maliciously de- signed to cause poor performance (Goodfellow et ...2015). Adversarial examples expose re- gions of the input space where the model performs poorly, which can ... See full document
6
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
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Keeping Consistency of Sentence Generation and Document Classification with Multi Task Learning
... novel multi-task learning method to maintain consistency among ...as multi-task learning for object detection and image caption ... See full document
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Analysis of Semi Supervised Learning Methods towards Multi Label Text Classification
... The commonly used performance evaluation measures for multi-label classifiers are broadly categorized in two groups namely bipartition-based and ranking-based [3]. Bipartition- based measures are again having two ... See full document
6
Named Entity Recognition for Nepali Text Using Support Vector Machines
... in text such as proper names, biological species, and temporal expressions into some predefined ...Nepali text, based on the Support Vector Machine (SVM) is presented which is one of machine learning ... See full document
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Hierarchical Transfer Learning for Multi label Text Classification
... a multi-label version of GRU-Att, GRU-Att-Multi, by replacing the out- put ...the multi-label model. To select class weights on the multi-label model us- ing a search over user-provided ... See full document
6
Learning Hierarchical Multi Category Text Classification Models
... the classification framework, review loss functions and derive a quadratic optimization prob- lem for finding the maximum margin model param- ...efficient learning algorithm relying a decomposition of the ... See full document
8
Multi-Task Learning for Classification with Dirichlet Process Priors
... of multi-task learning is different from that of meta ...the learning performance (i.e., classification accuracy) of each individual task, or to boost the performance of a new ... See full document
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Multinomial Adversarial Networks for Multi Domain Text Classification
... From Table 1, we can see that by adopting mod- ern deep neural networks, our methods achieve su- perior performance within the first two model cat- egories even without adversarial training. This is corroborated ... See full document
15
Multi Task Label Embedding for Text Classification
... Text classification is a common Natural Language Processing (NLP) issue that tries to infer the most appropriate label for a given sentence or docu- ment, for example, sentiment analysis, topic clas- ... See full document
9
A Unified Multi task Adversarial Learning Framework for Pharmacovigilance Mining
... Inspired by the success of stacked attentive RNN in solving other NLP tasks (Wu et al., 2016; Graves et al., 2013; Dyer et al., 2015; Prakash et al., 2016), we use the stacked GRU to encode the input text. The ... See full document
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