[PDF] Top 20 Neural Machine Translation into Language Varieties
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Neural Machine Translation into Language Varieties
... et al., 2016), Hindi and Urdu (Durrani et al., 2010), or Arabic dialects (Harrat et al., 2017). Notably, Pourdamghani and Knight (2017) build an unsu- pervised deciphering model to translate between closely related ... See full document
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Self Attentive Residual Decoder for Neural Machine Translation
... on Language Modeling To examine whether language modeling (LM) can benefit from the proposed method, we incorporate the residual connections into a neural ... See full document
14
Evaluating Layers of Representation in Neural Machine Translation on Part of Speech and Semantic Tagging Tasks
... Note that the parallel dataset is only used for training the NMT model. The classifier is then trained on the supervised data (described next) and all accuracies are reported on the English test sets. Semantic tagging ... See full document
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Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... other language pairs with different degree in specifying gender infor- mation in their written or spoken communication could be studied for the evaluation of debiasing in ... See full document
8
Converting Continuous Space Language Models into N Gram Language Models for Statistical Machine Translation
... network language models, or continuous-space language models (CSLMs), have been shown to improve the performance of statistical machine translation (SMT) when they are used for reranking ... See full document
6
Improving Sequence to Sequence Neural Machine Translation by Utilizing Syntactic Dependency Information
... encoder-decoder neural network which encodes a variable-length input sequence into a vector and decodes it into a variable-length ...tasks, Neural Machine Translation (NMT) model, which is ... See full document
9
Data Augmentation for Low Resource Neural Machine Translation
... sentence translation pairs with words occurring in diverse contexts, which is typically not avail- able in low-resource language ...these language pairs (Zoph et ...low-resource language pairs ... See full document
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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 ...(probabilistic translation dic- tionaries ... See full document
6
Encoding Source Language with Convolutional Neural Network for Machine Translation
... The Role of Guiding Signal It is slight sur- prising that the generic CNN can also achieve the gain on BLEU similar to that of BBN- JM, since intuitively generic CNN encodes the entire sentence and the representations ... See full document
11
Predicting Target Language CCG Supertags Improves Neural Machine Translation
... Neural machine translation (NMT) mod- els are able to partially learn syntactic in- formation from sequential lexical informa- ...target language syntax help NMT? 2) Is tight integration of ... See full document
12
An Empirical Study of Language Relatedness for Transfer Learning in Neural Machine Translation
... the case of German as a parent for Luxembour- gish is quite striking since the latter is known to be closely related to the former. Moreover us- ing German gives an additional improvement of around 2 BLEU points over ... See full document
5
Multimodal Neural Machine Translation for Low resource Language Pairs using Synthetic Data
... Sennrich et al. (2016) incorporated mono- lingual data on the target side to investi- gate two methods of filling the source side of the monolingual data. In the first method, they used a dummy source sentence for every ... See full document
10
Improving Japanese to English Neural Machine Translation by Paraphrasing the Target Language
... Recently, neural-network-based methods have gained considerable popularity in many natural language processing ...of machine translation, neural machine translation (NMT) ... See full document
6
Pragmatic Neural Language Modelling in Machine Translation
... Table 5 and Table 6 show the impact on transla- tion quality for the proposed normalisation schemes with and without an additional n-gram model. We note that when KenLM is used, no significant differ- ences are observed ... See full document
10
Semi Supervised Neural Machine Translation with Language Models
... To introduce repeatable results, we reproduced all experiments on famous WMT europarl-v7 for En–Fr pair. This corpus consists of almost 2M parallel sentences, so we were able to extract parallel data, test set, and ... See full document
8
English Indonesian Neural Machine Translation for Spoken Language Domains
... We use OpenSubtitles2018 (Lison et al., 2018) parallel corpus as our out-of-domain data and TEDtalk (Cettolo et al., 2012) as in-domain data. OpenSubtitles2018 corpus contains movie subti- tles which can represent ... See full document
6
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring
... unsupervised machine translation track of the WMT’19 news shared task from German to ...tistical machine translation (PBSMT) model and a pre-trained language model to combine word-level ... See full document
8
Multilingual Neural Machine Translation with Language Clustering
... on language family is consistent with the human knowledge, easy to understand, and does not change with respect to ...on language family does not cover all the languages in the world since some languages ... See full document
11
Deep Neural Language Models for Machine Translation
... It is worth mentioning another active line of re- search in building end-to-end neural MT systems (Kalchbrenner and Blunsom, 2013; Sutskever et al., 2014; Bahdanau et al., 2015; Luong et al., 2015; Jean et al., ... See full document
5
Survey and Analysis on Language Translator using Neural Machine Translation
... Neural machine Translation (NMT) is an empirical approach to the known process of Machine Translation that uses a large artificially constructed neural network to predict or to ... See full document
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