[PDF] Top 20 Source Language Adaptation for Resource Poor Machine Translation
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Source Language Adaptation for Resource Poor Machine Translation
... little adaptation, which works well for very closely related ...a resource-poor language (In- donesian or Spanish, pretending that Spanish is resource-poor) with a much larger ... See full document
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Source Language Adaptation Approaches for Resource Poor Machine Translation
... The hypothesis producers presented here are all based on statistical methods. In principle, we can also use some rule-based hypothesis producers to adapt Malay to Indonesian. For example, the number format of Malay is ... See full document
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Evaluating machine translation in a low resource language combination: Spanish Galician
... the source document to be processed by the three different MT ...thematic translation memory (TM) of 4315 translation units and a parallel corpus of 6 million words from the legal and administrative ... See full document
6
Unsupervised Source Hierarchies for Low Resource Neural Machine Translation
... adding source hierarchical informa- tion to neural machine translation has used super- vised ...model source syntax. Chen et al. (2017b) enriched source word representations by ... See full document
7
Neural Lattice Search for Domain Adaptation in Machine Translation
... The pseudocode is in Algorithm 1, and a graphical depiction in Figure 2. In the lattice (Figure 2(a)), arcs are annotated with phrases of one or more words indicating the target sides of phrases that were applied during ... See full document
6
Language and Translation Model Adaptation using Comparable Corpora
... statistical machine translation systems have relied on parallel bi-lingual data to train a translation ...a language model in the target ...statistical machine translation system ... See full document
10
Incremental Domain Adaptation for Neural Machine Translation in Low Resource Settings
... domain adaptation in the context of statistical and neural machine ...the source language of an NMT model to identify train- ing samples that are close to the new ...domain adaptation ... See full document
10
Low Resource Corpus Filtering Using Multilingual Sentence Embeddings
... uses language model and word trans- lation scores, with weights optimized to separate clean and synthetic noise ...both language pairs Sinhala–English and ...open source release 6 of the Zipporah ... See full document
6
Data Augmentation for Low Resource Neural Machine Translation
... a source and target sentence pair (S,T), we want to alter it in a way that preserves the semantic equivalence between S and T while diversifying as much as possible the training ...other language by that ... See full document
7
Cross lingual Induction of Selectional Preferences with Bilingual Vector Spaces
... the source language, which seems to run counter to our original motivation of developing methods for resource-poor ...verb translation, averaged over n rounds (n = 10 for 5 arguments, n ... See full document
9
Sentence Level Adaptation for Low Resource Neural Machine Translation
... statistical machine translation, Liu et ...the translation mem- ...non-domain-specific language model (LM) training data by compar- ing its cross-entropy with as domain-specific LM, while Duh ... See full document
9
Target Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation
... Neural Machine Translation (NMT) with multilingual corpora, training on the most related high-resource lan- guage only is often more effective than us- ing all data available (Neubig and Hu, ...low- ... See full document
6
Robust Document Representations for Cross Lingual Information Retrieval in Low Resource Settings
... for language modeling (source-side bitext, web crawled monolingual ...the machine translation system expects inputs that have been tokenized and ... See full document
9
Translation Based Projection for Multilingual Coreference Resolution
... projection, translation, and coreference resolu- tion in the resource-rich ...our translation-based approach and Camargo de Souza and Orasan’s (2011) approach, where annotations are projected via a ... See full document
11
Improved Statistical Machine Translation for Resource Poor Languages Using Related Resource Rich Languages
... were resource-poor until recently; this is changing quickly because of the increasing volume of EU parliament debates and the ever-growing European ...official language of the EU has turned out to be ... See full document
10
Small in Size, Big in Precision: A Case for Using Language Specific Lexical Resources for Word Sense Disambiguation
... on machine translation to fill in the lexical gaps between resource-rich and research-poor lan- guages (as with BabelNet) must only be a stopgap measure, and that work to grow and extend ... See full document
10
A Tagging style Reordering Model for Phrase based SMT
... statistical machine translation system, reordering is still a major problem for language pairs like Chinese-English, where the source and target language have significant word order ... See full document
10
Speech Translation System for Language Barrier Reduction
... their machine-learning models are trained on generic ...multi-domain translation services–that is, customizable solutions across multiple domains–and other organizations offer translation solutions ... See full document
6
Benefits of Data Augmentation for NMT based Text Normalization of User Generated Content
... Since NLP tools have originally been developed for and trained on standard language, these non- standard forms adversely affect their performance. One of the computational approaches which has been suggested to ... See full document
11
Source Language Features and Maximum Correlation Training for Machine Translation Evaluation
... • Combination of metrics based on machine learning. Kulesza and Shieber (2004) used SVMs to combine several metrics. Their method is based on the assumption that higher classification accuracy in discriminat- ing ... See full document
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