[PDF] Top 20 Neural Machine Translation of Text from Non Native Speakers
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Neural Machine Translation of Text from Non Native Speakers
... The second step involves applying the noise- inducing transformations using our collected statistics as a prior. We obtained parses for each sentence using the Berkeley parser (Petrov et al., 2006). The parse tree allows ... See full document
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QCRI@QALB 2015 Shared Task: Correction of Arabic Text for Native and Non Native Speakers’ Errors
... Arabic text. The corpus is composed of text that is pro- duced by native speakers as well as non-native speakers (Habash et ... See full document
5
Controlling Text Complexity in Neural Machine Translation
... comes from text simplification: using English simplifica- tion (first pipeline) outperforms Spanish simplifi- cation (second pipeline) according to BLEU and PCC, but not ...samples from many tasks ... See full document
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Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... Neural machine translation has significantly pushed forward the quality of the ...ness. Neural models are trained on large text corpora which contain biases and ...in neural ma- ... See full document
8
Paraphrasing Revisited with Neural Machine Translation
... approach from the perspective of neural machine translation, a new approach to machine transla- tion based purely on neural networks (Kalchbren- ner and Blunsom, 2013; Bahdanau ... See full document
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Vous or tu? Native and non native speakers of French on a sociolinguistic tightrope
... L1 speakers between those who knew additional languages with complex systems and the English-French bilinguals suggests that prior exposure to non-native languages can affect learners’ use of ... See full document
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Reliable Lexical Simplification for Non Native Speakers
... extracted from corpora of spoken text, such as subtitles, tend to correlate better with word familiar- ity than frequencies of other sources, given that the text in subtitles is mostly composed of ... See full document
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Target Foresight Based Attention for Neural Machine Translation
... in neural machine translation, an attention model is used to identify the aligned source words for a target word target foresight word in order to select translation con- text, but it ... See full document
11
Pre Translation for Neural Machine Translation
... The English word goalie is not translated to the correct German word Torwart, but to the German word Gott, which means god. One problem could be that we need to limit the vocabulary size in order to train the model ... See full document
9
Proficient beyond borders: assessing non-native speakers in a native speakers’ framework
... in native speakers and non-native speakers, Hulstijn (2011) claims that second or foreign language learners can reach the same level of higher language cognition (HLC) as native ... See full document
19
Native speakers’ assessment of (im)politeness of non-native speakers’ requests
... inferences from the text, it is important that the classification procedure be reliable in the sense of being consistent: different people should code the same text in the same way" (Weber cited ... See full document
18
Japanese Lexical Simplification for Non Native Speakers
... monolingual machine translation (Narayan and Gardent, 2014), word alignment approach (Coster and Kauchak, 2011; Paetzold and Specia 2013; Horn, et ... See full document
5
LIMSI’s participation to the 2013 shared task on Native Language Identification
... submission from LIMSI to the 2013 shared task on Native Language Identifica- tion (Tetreault et ...eleven native lan- guages (Blanchard et ...studying Native Language Identification in ...of ... See full document
6
On the Word Alignment from Neural Machine Translation
... in translation, and thus it is natural to ask why NMT yields worse alignment than the aligner F AST A LIGN in SMT, as shown in Table ...butions from both source and target sides, NMT may capture good ... See full document
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The acquisition of phrasal vocabulary by non native speakers of Spanish
... [r] ... See full document
114
fairseq: A Fast, Extensible Toolkit for Sequence Modeling
... FAIRSEQ is an open-source sequence model- ing toolkit that allows researchers and devel- opers to train custom models for translation, summarization, language modeling, and other text generation tasks. The ... See full document
6
Tutorial: De mystifying Neural MT
... Neural Statistical Machine Translation Neural Machine Translation Encoder Decoder Sequence-to-sequence learning: Encoder Sequence-to-sequence learning: Decoder Let’s use a simple NN for [r] ... See full document
84
Compliment and Compliment Responses: A Comparative Study between Dari and English Native Speakers
... and native English ...by native English speakers contained 50% acceptance, 45% percent mitigation and 3% rejection while the compliment responses produced by Syrians contained 67% acceptance, 33% ... See full document
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COMPOSITIONALITY/NON-COMPOSITIONALITY OF IDIOMS: NON-NATIVE SPEAKERS’ CONSTRAINTS TO COMPREHENSION
... and non- literal division (Giora, ...or non-literal meanings are activated first regardless of the contextual ...or non-literal ...the non- salient ones would then be activated by language ... See full document
10
NAVER Machine Translation System for WAT 2015
... In addition, we used a rule augmentation method which is known as syntax-augmented machine translation (Zollmann and Venugopal, 2006). Because the tree-to-string SMT makes some constraints on extracting ... See full document
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