[PDF] Top 20 Neural Structural Correspondence Learning for Domain Adaptation
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Neural Structural Correspondence Learning for Domain Adaptation
... a neural network model that marries together ideas from two prominent strands of research on domain adaptation through representation learning: structural correspondence ... See full document
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Structural Correspondence Learning for Parse Disambiguation
... Many current, effective natural language process- ing systems are based on supervised Machine Learning techniques. The parameters of such sys- tems are estimated to best reflect the character- istics of the ... See full document
9
Instance Weighting for Neural Machine Translation Domain Adaptation
... domain adaptation. However, it is challenging to be applied to Neural Machine Translation (NMT) directly, because NMT is not a linear ...and domain weighting with a dynamic weight ... See full document
7
Domain Adaptation of Neural Machine Translation by Lexicon Induction
... Prior work on using monolingual data to do data augmentation could be easily adapted to the do- main adaptation setting. Early studies on data- based methods such as self-enhancing (Schwenk, 2008; Lambert et al., ... See full document
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A Deep Learning Approach: Domain Adaptation for Large-Scale Sentiment Analysis
... _rst methods for building deep architectures (Bengio et al., 2006), along with Restricted Boltzmann Machines (RBMs) (Hinton et al., 2006). Once a stack of auto-encoders or RBMs has been trained, their parameters describe ... See full document
7
Neural Domain Adaptation for Biomedical Question Answering
... deep learning (DL) systems. Neural net- work models outperform traditional ap- proaches in domains where large datasets exist, such as SQuAD (≈ 100, 000 ques- tions) for Wikipedia ...a neural QA ... See full document
9
Frustratingly Easy Neural Domain Adaptation
... for domain adaptation such as the feature augmentation method of Daum´e III (2009) have mostly been considered for sparse binary-valued features, but not for dense real- valued features such as those used ... See full document
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Simplified Neural Unsupervised Domain Adaptation
... Recently, neural-network-based domain adap- tation algorithms have been successful, including domain adversarial methods (Ganin et ...a neural ver- sion of SCL still obtains near ... See full document
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Domain-Adversarial Training of Neural Networks
... source-target domain pair, we generate the mSDA represen- tations using a corruption probability of 50% and a number of layers of ...three learning algorithms (DANN, NN, and SVM) on these ...a ... See full document
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Iterative Dual Domain Adaptation for Neural Machine Translation
... NMT domain adaptation has attracted much ...and domain weighting methods with a dynamic weight learning ...unlabeled domain training samples based on their similarity to ... See full document
11
Metric Learning for Graph Based Domain Adaptation
... Most of the graph based SSL algorithms mentioned above concentrate primarily on the label inference part, i.e., assigning labels to nodes once the graph has already been constructed, with very little emphasis on ... See full document
10
Bi Transferring Deep Neural Networks for Domain Adaptation
... a structural correspon- dence learning (SCL) algorithm to train a cross- domain sentiment ...multi-task learning algorithm, alternating struc- tural optimization (ASO), proposed by Ando and ... See full document
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Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network
... learns domain invariant features by jointly optimizing the underlying feature layer from the main learning task and the domain label predic- ...main learning task is the re- lation type ... See full document
5
Event Detection and Domain Adaptation with Convolutional Neural Networks
... convolutional neural net- work (LeCun et ...deep learning recently, CNNs have been stud- ied extensively and applied effectively in vari- ous tasks: semantic parsing (Yih et ... See full document
7
Cross Language Text Classification Using Structural Correspondence Learning
... unsupervised domain adapta- tion problem by considering each language as a separate ...unsupervised domain adaptation, called structural correspondence learn- ...the structural ... See full document
10
Domain Adaptation with Structural Correspondence Learning
... Discriminative learning methods are widely used in natural language process- ...source domain to a resource-poor target domain. We introduce structural correspondence learning to ... See full document
9
Domain Adaptation for Authorship Attribution: Improved Structural Correspondence Learning
... learned correspondence features, and showed that replacing (rather than concatenating) the non-pivot features with the correspondence features generally yields better ... See full document
10
Sentiment Domain Adaptation with Multiple Sources
... SCL, domain adap- tation based on structural correspondence learn- ing (Blitzer et ...SFA, domain adap- tation based on spectral feature alignment (Pan et ...source domain sce- nario by ... See full document
10
Curriculum Learning for Domain Adaptation in Neural Machine Translation
... We show the effectiveness of our method on four tasks. Results show that curriculum learn- ing models can improve over the standard contin- ued training model by up to 3.22 BLEU points and can take better advantage of ... See full document
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Recursive Neural Structural Correspondence Network for Cross domain Aspect and Opinion Co Extraction
... deep adaptation models for comparison (Chen et ...target domain becomes more ...the structural correspondence network is indeed effective when integrated into ... See full document
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