[PDF] Top 20 Reinforced Training Data Selection for Domain Adaptation
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Reinforced Training Data Selection for Domain Adaptation
... of domain shifting where distribution mismatch in the data across domains greatly affect model ...ing data selection (TDS) has been proven to be a prospective solution for domain ... See full document
12
Towards Domain Adaptation for Parsing Web Data
... tional data which generally fits the testing domain, as mentioned ...in training as stand- alone ...the training data rather than selecting a subset, to bet- ter match the distribution ... See full document
8
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
PJIIT’s systems for WMT 2017 Conference
... model training, created adaptations of training settings for each language pair, and implemented BPE (subword units) for our SMT ...and data adaptation techniques were ...of domain ... See full document
6
Combining Active Learning and Partial Annotation for Domain Adaptation of a Japanese Dependency Parser
... We combined partial annotation with active learn- ing to adapt a Japanese dependency parser to new domains, and showed that active learning is not limited to single-domain settings. We showed that an entropy-based ... See full document
9
Regularized Training Objective for Continued Training for Domain Adaptation in Neural Machine Translation
... continued training (Luong and Manning, 2015), where a model is first trained on the out-of-domain corpus, and then that model is used to initialize a new model that is trained on the in-domain ... See full document
9
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
Parser Adaptation to the Biomedical Domain without Re Training
... biomedical data (SG-bio) fared worse than the original (SG-news), due probably in large part to the fact that the original training data was almost 100 times larger than our ... See full document
11
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
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
6
Self-Adaptation for Unsupervised Domain Adaptation
... labelled data in the target do- main for training is a common problem in domain ...vised domain adaptation method that com- bines projection and self-training based ...labelled ... See full document
10
Self Training for Enhancement and Domain Adaptation of Statistical Parsers Trained on Small Datasets
... combined training set should contain items that the seed dataset does ...combined training set surely includes inaccurate labels that might harm parser ...self- training data contain science, ... See full document
8
Cross domain textual geocoding: the influence of domain specific training data
... subject in a text is a process which is called geocoding [18]. More specifically, geocoding is the process of identifying and disambiguating references to geographic locations, called toponyms. The process of geocoding ... See full document
67
Data point selection for self training
... sparse data problems has gained a lot of at- tention in recent ...While training a generative parsing model on its own output (Charniak, 1997; Steedman et ...of domain adaptation (Bacchiani et ... See full document
6
Domain Adaptation with Unlabeled Data for Dialog Act Tagging
... require domain-specific tag sets, it is often useful to label utterances based on generic tags, and several tag sets have been developed for this purpose, ...get domain, or even the ... See full document
8
Cross Domain Detection of Abusive Language Online
... the training portion of X will also be smaller than the train- ing portion of Y , and it could be argued that the drop in performance is simply due to the model having less training ...more training ... See full document
6
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
Edit Distance: A New Data Selection Criterion for Domain Adaptation in SMT
... The experiments presented in this paper are car- ried out with the Moses toolkit (Koehn et al., 2007), a state-of-the-art open-source phrase- based SMT system. The translation and the re- ordering model relied on ... See full document
6
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 ... See full document
8
Learning Hidden Unit Contribution for Adapting Neural Machine Translation Models
... out-of-domain data, a situation which may easily happen in ...short training time and small memory foot- print make it a very attractive solution for do- main ... See full document
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