[PDF] Top 20 Frustratingly Easy Domain Adaptation
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Frustratingly Easy Domain Adaptation
... of domain adaptation is to develop learn- ing algorithms that can be easily ported from one domain to another—say, from newswire to biomed- ical ...“source” domain (say, newswire) but truly ... See full document
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Frustratingly Hard Domain Adaptation for Dependency Parsing
... to adaptation is to favor training examples that are similar to the ...the domain of each sentence based on POS n-grams in the ...target domain received higher weights, also without ... See full document
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Self-Adaptation for Unsupervised Domain Adaptation
... target domain is co-training (Blum and Mitchell, 1998), where the availability of multi- ple views of the feature space is ...target domain unlabelled ...get domain unlabelled instances, which makes ... See full document
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Domain Adaptation for MT: A Study with Unknown and Out of Domain Tasks
... desired domain for both NMT and ...using domain adaptation methods ...in- domain data such as system interpolation, instance weighting and data ...the domain of user translation ... See full document
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Transferable Attention for Domain Adaptation
... unsupervised domain adaptation based on ResNet-50 are shown in Table ...on easy transfer tasks, D → W and W → ...the domain discriminators might find fewer regions to align due to ... See full document
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Fast Easy Unsupervised Domain Adaptation with Marginalized Structured Dropout
... Unsupervised domain adaptation often re- lies on transforming the instance represen- tation. However, most such approaches are designed for bag-of-words models, and ignore the structured features present in ... See full document
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Sentiment Domain Adaptation with Multiple Sources
... tracted from the syntactic parsing results accord- ing to manually selected rules. Two rules are used to extract sentiment coherent relations. The first one is that two words are connected by coordi- nating conjunctions ... See full document
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A General Regularization Framework for Domain Adaptation
... Domain adaptation (DA) is an important problem that has received substantial attention in natural lan- guage processing (Blitzer et ...the frustratingly easy domain adapta- tion (FEDA) ... See full document
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Frustratingly Easy Semi Supervised Domain Adaptation
... source domain while the last segment corresponds to the target ...target domain features are transformed using these feature maps and the augmented fea- ture space so constructed is passed onto the un- ... See full document
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Frustratingly Easy Neural Domain Adaptation
... To test the effectiveness of our approach, we apply it to the slot sequence tagging task in a suite of 17 personal assistant domains. The goal is to find the correct semantic tagging of the words in a given user ... See full document
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Improving Domain Adaptation Translation with Domain Invariant and Specific Information
... the domain invariant features extracted from the training data are also beneficial to the out-of- domain translation ...News domain as our out-of-domain test ... See full document
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Domain Adaptation with BERT based Domain Classification and Data Selection
... novel domain adap- tation framework, in which the idea of domain- adversarial training is effectively executed in two separate ...BERT-based domain classifier is trained on data from differ- ent ... See full document
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Domain Adaptation for Relation Extraction with Domain Adversarial Neural Network
... We trained DANN to read the development set of bc to adapt to this domain. Although the gain seems to be small, the improvement is statistically significant (p-value < 0.01). We ran an instance- based sign test ... See full document
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Domain Independent Authorship Attribution without Domain Adaptation
... We have presented a set of features for authorship attribution in a domain independent setting. We have demonstrated that the features we calculate are effective in predicting authorship while being robust against ... See full document
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Metric Learning for Graph Based Domain Adaptation
... single domain, recent research has focused on Semi-Supervised Learning (SSL) algorithms, which learn from limited amounts of labeled data combined with widely available unlabeled ... See full document
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Domain Adaptation via Pseudo In Domain Data Selection
... As mentioned in Section 2.1, one established method is to rank the sentences in the general- domain corpus by their perplexity score accord- ing to a language model trained on the small in- domain corpus. ... See full document
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PJIIT’s systems for WMT 2017 Conference
... For instance, candidates with lower scores (Daumé III and Jagarlamudi 2011; Papineni et al. 2002; Mansour and Ney 2012) have higher relevancy to target specific domain. The size of the perplexity-based quasi ... See full document
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Simplified Neural Unsupervised Domain Adaptation
... One standard corpus used to develop new do- main adaptation algorithms is the Amazon senti- ment analysis dataset. 1 This corpus was created by Blitzer et al. (2007), but we use the version included in the ... See full document
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Domain Adaptation meets Active Learning
... unsupervised domain adaptation ...unsupervised domain adaptation tech- nique (na¨ıve instance weighting) we have used here for the first ...unsupervised domain adaptation ... See full document
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Can Domain Adaptation be Handled as Analogies?
... approached domain adaptation as a search for analogues of the selected features of a reduced BoW used to train a SVM ...new domain without retraining it with a new domain labelled ...new ... See full document
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