[PDF] Top 20 Dynamic Data Selection for Neural Machine Translation
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Dynamic Data Selection for Neural Machine Translation
... Regarding data selection for SMT, previous work has targeted two goals; to reduce model sizes and training times, or to adapt to new ...domains. Data selection methods for domain adaptation ... See full document
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Denoising Neural Machine Translation Training with Trusted Data and Online Data Selection
... is data selection for machine ...of data is based on cross entropy difference (CED) between an in-domain and an out-of-domain language ...ing data with CED according to an in-domain LM ... See full document
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Dynamically Composing Domain Data Selection with Clean Data Selection by “Co Curricular Learning” for Neural Machine Translation
... a dynamic data selection function, D φ λ (t, D), to return the top λ(t) of ex- amples in a dataset D sorted by a scoring func- tion φ at a training step ...potential data selection ... See full document
11
Improving Neural Machine Translation Models with Monolingual Data
... monolingual data have played a central role in statistical machine translation since the first IBM models (Brown et ...phrase-based translation models make strong independence as- sumptions, ... See full document
11
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
10
Data Augmentation for Low Resource Neural Machine Translation
... vision, data augmentation techniques are widely used to increase robustness and im- prove learning of objects with a limited number of training ...ing data is augmented by, for instance, horizon- tally ... See full document
7
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
Soft Contextual Data Augmentation for Neural Machine Translation
... contextual data augmentation, a simple yet effective data augmen- tation approach for ...training data by replacing a randomly chosen word in a sentence with a soft word, which is a probabilis- tic ... See full document
6
Automatic Threshold Detection for Data Selection in Machine Translation
... Data selection for Machine Translation (MT) rep- resents a standard domain adaptation technique with the aim of tackling the problem of select- ing from various general domain data the ... See full document
6
Adapting Neural Machine Translation with Parallel Synthetic Data
... parallel data is not strictly necessary for perform- ing domain adaptation: the usage of synthetic data has positive effects on the NMT ...synthetic data they automatically trans- lated a large ... See full document
10
Bi Directional Neural Machine Translation with Synthetic Parallel Data
... training data by swapping the source and target sentences of a parallel corpus and appending the swapped ver- sion to the ...target data, alleviating the issue of unknown words and reducing the vocabulary ... See full document
8
Zero Resource Neural Machine Translation with Monolingual Pivot Data
... Zero-shot neural machine translation (NMT) is a framework that uses source-pivot and target-pivot parallel data to train a source- target NMT ...monolingual data in the pivot language ... See full document
9
Active Learning for Interactive Neural Machine Translation of Data Streams
... 30%) data for fine- tuning the ...useful data. The QES and RS required more su- pervised data for achieving the comparable BLEU ...the data, we observed BLEU ... See full document
10
NAVER Machine Translation System for WAT 2015
... We used 1 million sentence pairs that are con- tained in train-1.txt of ASPEC-JE corpus for training the translation rule tables and NMT models. We also used 3 million Japanese sen- tences that are contained in ... See full document
5
Iterative Back Translation for Neural Machine Translation
... a data-hungry approach, requiring a large amount of parallel data to reach reasonable per- formance (Koehn and Knowles, ...parallel data, ...monolingual data in either the source or target ... See full document
7
Data augmentation using back translation for context aware neural machine translation
... (sentence-level translation), as reported in (Imamura et ...the data aug- mentation for context-aware NMT , and would also improve the context-aware models’ ability to cap- ture contexts because they, ... See full document
10
Dynamic Past and Future for Neural Machine Translation
... that neural ma- chine translation (NMT) models can bene- fit from explicitly modeling translated (P AST ) and untranslated (F UTURE ) source contents as recurrent states (Zheng et ...the dynamic up- ... See full document
11
Submodularity for Data Selection in Machine Translation
... parallel data, and billions of words of mono- lingual data for language ...Large data sets are often beneficial, but they do create certain other ...training data size but levels off after a ... See full document
11
Transductive Data Selection Algorithms for Fine Tuning Neural Machine Translation
... selecting data and fine-tuning; (ii) the usage of the data is less ef- ficient as a same sentence can be extracted multiple times (in different iterations); and (iii) using differ- ent models for each ... See full document
11
Adaptation Data Selection using Neural Language Models: Experiments in Machine Translation
... chine Translation (SMT) systems is the dearth of high-quality bitext in the domain of ...adaptation data selection: the idea is to use language models (LMs) trained on in-domain text to select ... See full document
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