[PDF] Top 20 Transductive Data Selection Algorithms for Fine Tuning Neural Machine Translation
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Transductive Data Selection Algorithms for Fine Tuning Neural Machine Translation
... the fine-tuning is the same used for building the BASE12 ...selected data there are several occur- rences) if BPE was not applied because it is infre- quent in the general domain ... See full document
11
Dynamic Data Selection for Neural Machine Translation
... Intelligent selection of training data has proven a successful technique to simul- taneously increase training efficiency and translation performance for phrase-based machine ... See full document
11
Instance Selection for Machine Translation using Feature Decay Algorithms
... set. Transductive retrieval selects train- ing data close to the test set given a parallel corpus and a test ...that transductive retrieval of the training set for statistical machine ... See full document
12
Extracting In domain Training Corpora for Neural Machine Translation Using Data Selection Methods
... all fine-tuning results are below the fully trained models with all data from the previ- ous ...all data has access to both the generic and domain vocabulary, the fine-tuned models are ... See full document
8
Dynamically Composing Domain Data Selection with Clean Data Selection by “Co Curricular Learning” for Neural Machine Translation
... There has already been rich research in CL for NMT. Fine-tuning a baseline on in-domain paral- lel data is a good strategy (Thompson et al., 2018; Sajjad et al., 2017; Freitag and Al-Onaizan, 2016). ... See full document
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Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection
... trusted data (Section 5.1) when the background data is very ...that fine-tuning on domain data improves domain test sets, but it is also known that it may hurt test sets that are out of ... See full document
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Improving Neural Machine Translation Models with Monolingual Data
... monolingual data for pure neural machine translation architec- tures was first investigated by (Gülçehre et ...training data, but the lan- guage model parameters are fixed during the ... See full document
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Exploiting Multilingualism through Multistage Fine Tuning for Low Resource Neural Machine Translation
... En-XX data, we sep- arately used two parallel corpora with two dif- ferent target ...free translation task English–Japanese corpus (KFTT En-Ja), 8 consist- ing of Japanese-to-English translations: 440,288 ... See full document
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An Empirical Comparison of Domain Adaptation Methods for Neural Machine Translation
... “Mixed fine tuning” can address the over-fitting problem of “Fine ...both fine-tuning and mixed fine-tuning tends to con- verge after 1 epoch of training, and thus we ... See full document
7
Selecting Artificially Generated Sentences for Fine Tuning Neural Machine Translation
... thentic data, as models built with different propor- tions of authentic and synthetic data achieve si- milar or even better performance than those fine- tuned with authentic pairs ...accurate ... See full document
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Adapting Neural Machine Translation with Parallel Synthetic Data
... instance selection method and applied it to collect the most ade- quate sentences for translating a corpus from a specific ...and fine-tuned a NMT sys- tem, originally trained on a more general ...single ... See full document
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Regularization techniques for fine tuning in neural machine translation
... for neural ma- chine translation where an existing model trained on a large out-of-domain dataset is adapted to a small in-domain ...to neural ma- chine translation, obtaining improvements on ... See full document
6
A Comparative Evaluation of Data driven Models in Translation Selection of Machine Translation
... Selection errors taking place in LSA and PLSA models were caused mainly by the fol- lowing reasons. First of all, the size of vocab- ulary should be limited by computation com- plexity. In this experiment, we ... See full document
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Leveraging resource management for efficient performance of Apache Spark
... Big Data with MapReduce, for example: MapRe- duce consumes very high communication, it makes a selective access to input data, and it is wasteful ...of data to be ...of data, but Hadoop ... See full document
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Automatic Threshold Detection for Data Selection in Machine Translation
... Biomedical Translation Task of the Sec- ond Conference on Machine Translation (WMT ...ing data selection for Machine Transla- tion via Paragraph Vector and a Feed For- ward ... See full document
6
Search Aware Tuning for Machine Translation
... Parameter tuning has been a key problem for ma- chine translation since the statistical ...existing tuning algorithms treat the decoder as a black box (Och, 2003; Hopkins and May, 2011; ... See full document
11
Transductive Minimum Error Rate Training for Statistical Machine Translation
... test data is that we expect the estima- tion procedure biases towards the test data when incorporated in the learning ...development data also plays an impor- tant role in the learning ...ment ... See full document
8
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... proper translation has to be derived from ...the translation sys- tem is gender biased, the context is disregarded, while if the system is neutral, the translation is cor- rect (since it has the ... See full document
8
Multi Source Neural Machine Translation with Missing Data
... have data in all of the languages that go into our multi- source ...on data that con- tains all of the source ...missing data, such situations using an incomplete multilingual corpus in which some ... See full document
8
Exploiting Monolingual Data at Scale for Neural Machine Translation
... back translation (briefly, BT) (Sennrich et ...monolingual data is well u- tilized by NMT through BT and its variants (Sen- nrich et ...monolingual data is very ...monolingual data, with a ... See full document
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