[PDF] Top 20 Learning Robust Representations of Text
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Learning Robust Representations of Text
... Deep neural networks have achieved remark- able results across many language processing tasks, however these methods are highly sen- sitive to noise and adversarial attacks. We present a regularization based method for ... See full document
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A Robust Self Learning Framework for Cross Lingual Text Classification
... by learning just from available English samples, but then makes predictions on unlabeled non-English samples and a part of those samples with high confidence prediction scores are repurposed to serve as labeled ... See full document
5
Jointly Embedding Entities and Text with Distant Supervision
... The robust terminologies available in the biomedical domain have been instrumental to sev- eral recent annotation–based ...document text) as input for skip-gram ...learn representations of the MeSH ... See full document
12
Robust Morphological Tagging with Word Representations
... to representations obtained from ...representation learning (Ando and Zhang, 2005; Blitzer et ...representation learning for DA, we attempt to include word representations into the ... See full document
11
Adversarial Learning of Privacy Preserving Text Representations for De Identification of Medical Records
... Although initially conceived for use in domain adaptation, DANNs and similar adversarial deep learning models have recently been used to obfus- cate demographic attributes from text (Elazar and Goldberg, ... See full document
11
Learning Text Representations for 500K Classification Tasks on Named Entity Disambiguation
... testing text representation and classification ...deep learning methods provide end-to-end algorithms to learn both representations and classifiers jointly (LeCun et ...fact, learning ... See full document
10
Bayesian Optimization of Text Representations
... of text that differ in relatively uninteresting ...In learning word embed- dings, distributional similarity approaches have been shown to perform competitively with neural network models when the ... See full document
6
Robust Anomaly Detection in Videos Using Multilevel Representations
... supervised learning are costly to obtain and are prone to human ...unsupervised learning can utilize huge un- labeled video archives on a daily basis, this is the approach we take to design our ... See full document
8
Evaluation of N-gram Text Representations for Automated Essay-Type Grading Systems
... Vantage Learning [20] combined approaches are treated as a “committee of judges,” and “potential scores” were proposed by building on the strategies utilized by PEG, IEA, and e-rater, IntelliMetric™, developed by ... See full document
7
Robust Text Classifier on Test Time Budgets
... for learning a robust text classification model that achieves high accuracy under different selection bud- gets ...of text, relevant for the target ...benchmark text classification ... See full document
6
A Probabilistic Approach to Text Generation of Human Motions extracted from Kinect Videos
... intermediate representations which correspond to the se- mantics of human motions and bridge the gap between time- series data and natural language ...machine learning for the correspondence between ... See full document
5
Evaluating Discourse in Structured Text Representations
... a text and is helpful in many NLP tasks. Learning latent representations of discourse is an attractive alternative to acquiring expen- sive labeled discourse ...for text classification that ... See full document
8
Language Understanding for Text based Games using Deep Reinforcement Learning
... reinforcement learning for training, our work has important ...of text, we use Long Short-Term Memory networks to auto- matically learn useful representations for arbitrary text ...learnt ... See full document
11
Active Learning with Rationales for Text Classification
... the learning curves for random sampling on four text classification datasets with binary and tf-idf representations and using multinomial na¨ıve Bayes, logistic regression, and support vector ma- ... See full document
11
Found in Translation: Learning Robust Joint Representations by Cyclic Translations between Modalities
... Early work on sentiment analysis focused primarily on writ- ten text (Pang, Lee, and Vaithyanathan 2002; Pang and Lee 2008; Socher et al. 2013). Recently, multimodal senti- ment analysis has gained more research ... See full document
8
Learning Distributed Representations for Multilingual Text Sequences
... than their English counterparts and there is gener- ally a large number of compound words in German. These phenomena are evidenced by the fact that the German vocab size is about twice of that of English (95K versus 43K) ... See full document
7
Learning Representations for Text level Discourse Parsing
... of text-level discourse ...deep learning architectures. To train more expressive representations that capture communicative functions and se- mantic roles of discourse units and rela- tions between ... See full document
6
Gov2Vec: Learning Distributed Representations of Institutions and Their Legal Text
... model text to understand politi- cal processes (Grimmer 2010; Roberts et ...a learning procedure similar to Structured Gov2Vec to streaming documents to learn represen- tations of documents that are similar ... See full document
6
Towards Robust and Privacy preserving Text Representations
... for learning the main task, but for the auxiliary task, we start with standard loss backpropagation, however gradients are reversed in sign when they reach ... See full document
6
What’s in a Domain? Learning Domain Robust Text Representations using Adversarial Training
... e.g., text classifiers that work well on texts from newswire to wikis to ...be robust to domain variation, such that domain-general concepts are learned in preference to domain-specific phenomena, which ... See full document
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