unsupervised domain adaptation
Task Refinement Learning for Improved Accuracy and Stability of Unsupervised Domain Adaptation
... Pivot Based Language Modeling (PBLM) (Ziser and Reichart, 2018a), combining LSTMs with pivot-based methods, has yielded significant progress in unsupervised domain adaptation. However, this approach ...
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A Hassle Free Unsupervised Domain Adaptation Method Using Instance Similarity Features
... of unsupervised domain adaptation is to “couple” the learning of weights for target-specific features with that of common ...of domain- specific features and distributional difference be- ...
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Unsupervised Domain Adaptation Based on Source-Guided Discrepancy
... Note that the above difference of the theoretical guaran- tees does not come from the inherent difference of these es- timators. This is because we adopted the analysis based on the Rademacher complexity, which has not ...
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Unsupervised Domain Adaptation for Clinical Negation Detection
... get domain since its hyper-parameter is tuned on the source ...real unsupervised domain adaptation scenario it is not possible to tune this parameter on the target domain, so for this ...
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Exploiting Local Feature Patterns for Unsupervised Domain Adaptation
... to domain-shift, as we can not manually label sufficient training data that cover di- verse application domains (Csurka 2017; Zhou et ...scenario, unsupervised domain adaptation methods at- ...
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Use of Combined Topic Models in Unsupervised Domain Adaptation for Word Sense Disambiguation
... the unsupervised domain adaptation for WSD (Shinnou and Sasaki, ...target domain corpus, and topic features con- structed from the topic model are added to train- ing data in both source and ...
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Fast Easy Unsupervised Domain Adaptation with Marginalized Structured Dropout
... Unsupervised domain adaptation is a fundamen- tal problem for natural language processing, as we hope to apply our systems to datasets unlike those for which we have ...for domain adap- tation ...
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Unsupervised Domain Adaptation for Neural Machine Translation with Domain Aware Feature Embeddings
... of Domain- Aware Feature Embedding (DAFE) that performs unsupervised domain adaptation by disentangling representations into different ...both domain and task embedding ...
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Graph Adaptive Knowledge Transfer for Unsupervised Domain Adaptation
... shallow domain adaptation approaches to evaluate the effectiveness of our algorithm as follows: Geodesic Flow Kernel (GFK) [24], Joint Distribution Adaptation (JDA) [3], Closest Common Space Learning ...
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Unsupervised domain adaptation for the automated segmentation of neuroanatomy in MRI: a deep learning approach
... for unsupervised domain adaptation which improves performance in challenging cross-domain applications without requiring any additional annotations on the target ...better domain ...
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Unsupervised Domain Adaptation of Contextualized Embeddings for Sequence Labeling
... state-of-the-art unsupervised domain adaptation method (Yang and Eisenstein, 2016), without taking any explicit steps to adapt to the target domain of Early Mod- ern ...pervised ...
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Unsupervised Domain Adaptation by Matching Distributions Based on the Maximum Mean Discrepancy via Unilateral Transformations
... Semi-supervised domain adaptation methods have been developed for the case where labeled data in the tar- get domain are ...Therefore, unsupervised domain adap- tation, which does not ...
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Importance weighting and unsupervised domain adaptation of POS taggers: a negative result
... Importance weighting is a generalization of various statistical bias correction tech- niques. While our labeled data in NLP is heavily biased, importance weighting has seen only few applications in NLP, most of them ...
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Adaptive Ensembling: Unsupervised Domain Adaptation for Political Document Analysis
... the unsupervised loss. Because the unsupervised loss effectively regularizes the source domain repre- sentations to work well in the target domain (Laine and Aila, 2017), performance degrades ...
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Unsupervised Domain Adaptation for Word Sense Disambiguation using Stacked Denoising Autoencoder
... source domain; and then, classify the test data from target domain by the classifier (as represented by source tar- get ...six domain adaptation patterns: (1) PB OC, (2) OC PB, (3) OC PN, (4) ...
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Self-Adaptation for Unsupervised Domain Adaptation
... 12 domain-pairs Self- Adapt is obtaining the best performance, the dif- ference of performance between DANN and Self- Adapt is not statistically ...8 domain- pairs, it is noteworthy that MT-Tri is using a ...
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Simplified Neural Unsupervised Domain Adaptation
... This work evaluated on the widely-used Ama- zon sentiment dataset from Blitzer et al. (2007). However, we believe that future work on do- main adaptation should phase out the use of this dataset. 3 The test set ...
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Unsupervised Domain Adaptation based on Text Relatedness
... target domain ) simply extracts all the words that can be found on a cor- pus from , minus the words that are consid- ered as “stop words”, and are filtered by using a stop word ...
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Unsupervised Domain Adaptation for Joint Segmentation and POS Tagging
... Modifications can be made to the standard self-training process for domain adaptation to ad- dress the difference in source and target distributions (Margolis, 2011). In Tan et al. (2009), the weights on ...
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Unsupervised Adversarial Domain Adaptation for Implicit Discourse Relation Classification
... exploit domain adapta- tion from explicit relations (Ji et ...an unsupervised adversarial do- main adaptive network equipped with a recon- struction ...for unsupervised domain ...
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