[PDF] Top 20 Word Representation Models for Morphologically Rich Languages in Neural Machine Translation
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Word Representation Models for Morphologically Rich Languages in Neural Machine Translation
... convolutional neural networks over character sequences, as part of models of part of speech tagging (Santos and Zadrozny, 2014), named entity recognition (Ma and Hovy, 2016; Chiu and Nichols, 2015), lan- ... See full document
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Translating Between Morphologically Rich Languages: An Arabic to Turkish Machine Translation System
... on machine translation sys- tem for a new low-resourced language pair Arabic- Turkish in news domain which is the first effort for this language pair to the best of our ...of-the-art neural ... See full document
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Compositional Representation of Morphologically Rich Input for Neural Machine Translation
... ity of the model. In a standard architecture, like ours, the source and target embedding matrices ac- tually account for the vast majority of the network parameters. The vocabulary size also plays an important role when ... See full document
7
Character Aware Decoder for Translation into Morphologically Rich Languages
... Neural machine translation (NMT) sys- tems operate primarily on words (or sub- words), ignoring lower-level patterns of ...convolutional neural networks that operate on the spelling of a ... See full document
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Identifying main obstacles for statistical machine translation of morphologically rich South Slavic languages
... chine translation system is to identify con- crete problems causing translation errors and address ...involved languages and differences be- tween ...statistical machine transla- tion systems ... See full document
8
Grapheme level Awareness in Word Embeddings for Morphologically Rich Languages
... with morphologically rich ...of languages that are agglutinative, represented by non-alphabetic scripts, or both, Korean as a case ...for neural word embedding that utilizes ... See full document
7
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 ...consequence, ... See full document
8
Enriching Morphologically Poor Languages for Statistical Machine Translation
... with rich Turkish morph tags on the target side, but improvement was gained only after augmenting the generation process with morphotac- tical ...a Word Sense Disambiguation ... See full document
8
Deep Neural Networks for Syntactic Parsing of Morphologically Rich Languages
... Morphologically rich languages (MRL) are languages in which much of the struc- tural information is contained at the word- level, leading to high level word-form ...tive ... See full document
6
Incorporating Word and Subword Units in Unsupervised Machine Translation Using Language Model Rescoring
... the morphologically rich characteristics of German and Czech (Tsarfaty et ...Both word-level and subword-level neural ma- chine translation (NMT) models are applied in this task ... See full document
8
Character Composition Model with Convolutional Neural Networks for Dependency Parsing on Morphologically Rich Languages
... convolutional neural network to compose word representations from ...the word-lookup model, especially for parsing agglutinative ...pre-trained word embeddings from extra ... See full document
7
LAMB: A Good Shepherd of Morphologically Rich Languages
... (low-dimensional) word representations in vec- tor space, have serious practical ...derresourced languages. Second, morphologically rich languages (MRLs) are a challenge for stan- dard ... See full document
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Addressing word order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages
... get languages either by using Byte Pair Encoding (BPE) as basic input representation units (Nguyen and Chiang, 2017) or character-level NMT sys- tem (Lee et ...of word order di- vergence and its ... See full document
6
Achieving Open Vocabulary Neural Machine Translation with Hybrid Word Character Models
... on neural ma- chine translation (NMT) has used quite restricted vocabularies, perhaps with a subsequent method to patch in unknown ...novel word- character solution to achieving open vo- cabulary ... See full document
10
Neural Machine Translation of Logographic Language Using Sub character Level Information
... Some studies have performed NMT tasks using various sub-word “equivalents”. For instance, Du and Way (2017) trained factored NMT mod- els using “Pinyin” 3 sequences on the source side. Unfortunately, they did not ... See full document
9
On the Word Alignment from Neural Machine Translation
... duce word alignment from general NMT models and answer a fundamental question that how much word alignment NMT models can learn (§ ...a word align- ment model between a pair of source ... See full document
11
Word Representations in Factored Neural Machine Translation
... The encoder and the attention mechanism of the Factored NMT are the same as the standard NMT model. However, the decoder has been modified to produce multiple outputs. The two outputs are constrained to have the same ... See full document
12
Phrase Based Backoff Models for Machine Translation of Highly Inflected Languages
... other languages such as Roma- nian (Fraser and Marcu, 2005) in order to de- crease word alignment error ...the word alignment training procedure to interpolate counts based on the different lev- els ... See full document
8
A Hybrid Morpheme Word Representation for Machine Translation of Morphologically Rich Languages
... ence translation, and the outputs of three SMT sys- tems (m-system, w-system, and ourSystem), which were shown in different order for each example and were named sys1, sys2 and sys3 (by order of ap- ... See full document
10
Word Translation Prediction for Morphologically Rich Languages with Bilingual Neural Networks
... based translation models (Gim- pel and Smith, 2008; Mauser et ...These models can ex- ploit a boundless context of the input text, but they assume that target words can be predicted in- dependently ... See full document
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