[PDF] Top 20 Frustratingly Easy Semi Supervised Domain Adaptation
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Frustratingly Easy Semi Supervised Domain Adaptation
... multi- domain setting. Prior work on semi-supervised approaches to domain adaptation also exists in lit- ...of domain adaptation. Co-adaptation (Tur, 2009), a ... 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
5
Transferable Curriculum for Weakly-Supervised Domain Adaptation
... weakly-supervised domain adaptation, extending from curriculum learning (Bengio et ...weakly-supervised domain adaptation: sample noises of the source domain and ... See full document
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Unsupervised adaptation of PLDA models for broadcast diarization
... model adaptation approach to deal with data variability for speaker diarization in a broadcast ...the domain mismatch by means of supervised model adaptation ...unsupervised adaptation ... See full document
13
Hybrid Method of Semi-supervised Learning and Feature Weighted Learning for Domain Adaptation of Document Classification
... another domain. In addition to the emo- tion analysis, supervised learning such as morpho- logical analysis (Mori, 2012), parsing (Sagae and Tsujii, 2007), word sense disambiguation (Shinnou et ...the ... See full document
8
Domain Adaptation with Adversarial Training and Graph Embeddings
... compare supervised vs. semi-supervised (L ...the semi-supervised model for all batches over the two ...the supervised mod- ...our semi-supervised approach can ... See full document
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Semi Supervised QA with Generative Domain Adaptive Nets
... Question Answering. Various neural models based on attention mechanisms (Wang and Jiang, 2016; Seo et al., 2016; Xiong et al., 2016; Wang et al., 2016; Dhingra et al., 2016; Kadlec et al., 2016; Trischler et al., 2016b; ... See full document
11
On the Challenges of Translating NLP Research into Commercial Products
... like domain adaptation, semi-supervised learning and transfer learning (Pan and Yang, 2010) are extremely relevant to address the prob- lem of data availability for industry ... See full document
5
Semi-supervised adaptation of RNNLMs by fine-tuning with domain-specific auxiliary features
... for domain adaptation of RNNLMs are latent Dirichlet allocation (LDA) [15] features ex- tracted from the ...for semi-supervised adapta- tion, aimed at compensating for the missing features and ... See full document
5
Strong Baselines for Neural Semi Supervised Learning under Domain Shift
... For the high-data setup (Table 4) results are simi- lar. Disagreement, however, is only favorable in the low-data setups; the effect of avoiding easy points no longer holds in the full data setup. Classic tri- ... See full document
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Adaptive Semi supervised Learning for Cross domain Sentiment Classification
... the domain discrepancy, they do not explic- itly minimize the distance between distributions, and some of them highly rely on the selection of pivot ...reducing domain difference. In their model, a sub- ... See full document
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A Survey on Semi Supervised Image to Video Adaptation for Video Action Recognition
... knowledge adaptation from images to videos have shown better performance in application of cross media recognition and ...Knowledge adaptation, also known as transfer learning, aims to propagate the ... See full document
7
Type Supervised Domain Adaptation for Joint Segmentation and POS Tagging
... type-supervised domain adaptation for joint Chinese word segmentation and POS-tagging, making use of domain- specific tag dictionaries and only un- labeled target domain data to improve ... See full document
10
Semi-supervised vs. Cross-domain Graphs for Sentiment Analysis
... and easy extension to multi-class classification (Zhu et ...the adaptation of a widely used Label Propagation (LP ) algorithm (Zhu and Ghahramani, 2002) to semi-supervised and ... See full document
8
Semi supervised Convolutional Networks for Translation Adaptation with Tiny Amount of In domain Data
... no domain knowledge about the training data, when we have only 100 sentences in the test domain, the semi-supervised CNN classi- fier can still select a good in-domain subset and ... See full document
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Mine the Easy, Classify the Hard: A Semi Supervised Approach to Automatic Sentiment Classification
... a semi-supervised approach to senti- ment classification where we first mine the unambiguous reviews using spectral tech- niques and then exploit them to classify the ambiguous reviews via a novel com- ... See full document
9
Unsupervised and Semi Supervised Morphological Analysis for Information Retrieval in the Biomedical Domain
... The maximization step simply compute the translation probabilities of a Kanji sequence into a letter sequence. For example, for the Kanji 菌 (’bacteria’), there may exist one entry in δ associating it with bactérie, one ... See full document
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Semi supervised Stochastic Multi Domain Learning using Variational Inference
... or domain adaptation (Blitzer et ...the domain of instances in the training data, and the domain is treated as a discrete in- dicator ...with domain labels not always available, or ... See full document
12
Fast Easy Unsupervised Domain Adaptation with Marginalized Structured Dropout
... On the specific problem of sequence labeling, Xiao and Guo (2013) proposed a supervised do- main adaptation method by using a log-bilinear language adaptation model. Dhillon et al. (2011) presented a ... See full document
7
Frustratingly Easy Neural Domain Adaptation
... K domain specific LSTMs with generic embeddings (KD&E) which is very close to Ando and Zhang (2005) achieves per- formance gain from 1D&E, but fail to provide any improvement from ...different ... See full document
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