[PDF] Top 20 Entropy based Training Data Selection for Domain Adaptation
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Entropy based Training Data Selection for Domain Adaptation
... 1998). Based on this property, we define a similarity measure, Sim(s, C), between a training sen- tence s and the test corpus C as the average of DLG scores of substrings in s, as shown in Eq ... See full document
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Sentiment Domain Adaptation with Multiple Sources
... the training stage, we extract two kinds of sentiment models, ...the domain-specific models, from the da- ta of multiple source domains using multi-task ...source domain. The domain-specific ... See full document
10
Edit Distance: A New Data Selection Criterion for Domain Adaptation in SMT
... when data selection model can best benefit the in-domain transla- ...tion. Based on the investigation of the state-of- the-art similarity metrics, we propose edit dis- tance as a new ... See full document
6
Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation
... Data selection is an effective approach to domain adaptation in statistical ma- chine ...for data se- lection: while the improvements are var- ied ...in-domain data and ... See full document
6
Domain Adaptation with Adversarial Training and Graph Embeddings
... labeled data. However, obtain- ing labeled data is a big challenge in many real-world ...labeled data from a related domain, but it has to deal with the shift in data distribu- tions ... See full document
11
Unsupervised training of maximum entropy models for lexical selection in rule based machine translation
... ing data, then it assumes that all outcomes —that is, all possible translations— are equally ...maximum entropy has been applied to the problem of lexical selection before; in particular, Berger et ... See full document
8
Parser Adaptation to the Biomedical Domain without Re Training
... Nonetheless, there were substantial variations in the strength of the improvement attained, with the weak performance of the Berkeley Parser be- ing a notable disappointment. Several differences could be invoked to ... See full document
11
Domain Adaptation for Dependency Parsing via Self Training
... out-of-domain data, cf. (Le Roux et al., 2012). However, none of the dependency-based systems used self-training in the SANCL 2012 shared ...self- training for dependency parsing was ... See full document
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Domain Adaptation of Maximum Entropy Language Models
... heuristically based on light tuning on develop- ment set ...and domain-specific data, the Gaussian pri- ors were fixed for each hierarchy node ...hierarchical adaptation scheme, also ... See full document
6
Extracting In domain Training Corpora for Neural Machine Translation Using Data Selection Methods
... that data selec- tion does not yield as much gain for the NMT as it did for ...mostly data selection of 2M or ...MultiUN data combined with the seed, which is balanced in the same way as the ... See full document
8
Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation
... Supervised domain adaptation—where a large generic corpus and a smaller in- domain corpus are both available for training—is a challenge for neural ma- chine translation ...continue ... See full document
9
Combining Active Learning and Partial Annotation for Domain Adaptation of a Japanese Dependency Parser
... this data by allowing annotators to focus on only the most informative training ...for domain adaptation of depen- dency parsers, not just in single-domain ...that ... See full document
9
PJIIT’s systems for WMT 2017 Conference
... news data sets have a rather a wide domain, but rather not as wide-ranging in topic as the variety of WMT permissible ...defined domain, this presents another considerable ...of domain ... See full document
6
Overcoming Catastrophic Forgetting During Domain Adaptation of Neural Machine Translation
... for training neu- ral networks to learn a new task without for- getting previously learned ...continued training in NMT (see §3): Our first task is to translate general-domain sen- tences, and our ... See full document
7
Fast Domain Adaptation of SMT models without in Domain Parallel Data
... parallel data. As expected, when an in- domain corpus is used both for training as well as for optimizing the log-linear parameters, the pefor- mance is much higher than those systems that do not use ... See full document
10
Reinforced Training Data Selection for Domain Adaptation
... different data and ...of) training instance at each step, where previous decision should influence later ...a training sample and the tar- get domain, and to guide the selection process ... See full document
12
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
8
Domain Adaptation with BERT based Domain Classification and Data Selection
... between training and test ...different domain. In this paper, we present a novel two-step domain adaptation framework based on curriculum learning and domain-discriminative ... See full document
8
Sentence Embedding for Neural Machine Translation Domain Adaptation
... sentence selection methods for PBSMT were also used as baselines: Axelrod et ...cross- entropy difference as criterion; Chen et ...out-of-domain data to create a corpus the same size as that ... See full document
7
Embedding Semantic Similarity in Tree Kernels for Domain Adaptation of Relation Extraction
... (ACE) data, we evaluate the impact of convolu- tion tree kernels embedding lexical semantic sim- ...2005 data, which exploits kernels, structures and similarities for do- main ... See full document
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