[PDF] Top 20 Graph Based Translation Memory for Neural Machine Translation
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Graph Based Translation Memory for Neural Machine Translation
... subsets based on the averaged similarity of each sentence in the retrieved trans- lation ...the translation memory indeed brings improvements of translation quality over all similarity ... See full document
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Graph based Learning for Statistical Machine Translation
... the graph. This choice is theoretically motivated—a similarity graph for regression should have not only “sources” (good nodes with high value of r) but also “sinks” (counterparts for the ...and ... See full document
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Dynamically Integrating Cross Domain Translation Memory into Phrase Based Machine Translation during Decoding
... the translation probability (lexical and phrase transfer in both direc- tions) and two more feature weights for the phrase penalty (details will be specified later in Section ... See full document
11
Integrating Translation Memory into Phrase Based Machine Translation during Decoding
... Statistical machine translation (SMT), especially the phrase-based model (Koehn et ...professional translation because its output quality is still far from satis- ... See full document
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Byte based Neural Machine Translation
... character- based neural machine translation with a diversity of languages, including languages as ...byte-based neural machine translation ...character ... See full document
5
Character based Neural Machine Translation
... better translation model at various levels, which seems to include better alignment, reordering, morphological generation and disam- ...character-based neural MT system is capable of achieving com- ... See full document
5
Forest Based Neural Machine Translation
... Second, compared with the cases that only using the 1-best constituent trees, using packed forests yields statistical significantly better results for the SoE and SoA frameworks. This shows the effectiveness of using ... See full document
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Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
... In the first sentence, the only difference is in the choice of the preposition for the argument Mark . Note that the argument is correctly assigned to role A2 (‘Buyer’) by the semantic role labeler. The BiRNN model ... See full document
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Generalizing Back Translation in Neural Machine Translation
... Back-translation — data augmentation by translating target monolingual data — is a crucial component in modern neural machine translation (NMT). In this work, we refor- mulate ... See full document
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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
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Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop
... English-Indonesian Neural Machine Translation for Spoken Language Domains Meisyarah Dwiastuti.. Improving Neural Entity Disambiguation with Graph Embeddings Özge Sevgili, Alexander Panch[r] ... See full document
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Incorporating Word Reordering Knowledge into Attention based Neural Machine Translation
... IBM Models (Brown et al., 1993) depict- ed the word reordering knowledge as position- al relations between source and target word- s. Koehn et al. (2003) proposed a distortion model for phrase-based SMT ... See full document
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Graph Convolutional Encoders for Syntax aware Neural Machine Translation
... of a word and its position in a sentence. Since the original BoW encoder captures the linear order- ing information only in a very crude way (through the position embeddings), the structural informa- tion provided by GCN ... See full document
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Neural Machine Translation with Source Side Latent Graph Parsing
... Neural Machine Translation (NMT) is an active area of research due to its outstanding empiri- cal results (Bahdanau et ...improve translation accu- racy (Eriguchi et ... See full document
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Alignment Based Neural Machine Translation
... as alignment. While we use larger vocabularies compared to the attention-based system, we ob- serve incorrect translations of rare words. E.g., the German word ¨Olknappheit in sentence 3 oc- curs only 7 times in ... See full document
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Memory enhanced Decoder for Neural Machine Translation
... the memory cell in LSTM(Hochreiter and Schmid- huber, ...(“a memory cell”) can be accessed by the decoding RNN with content-based addressing for both reading and writing during the decoding ...of ... See full document
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Memory Augmented Neural Networks for Machine Translation
... machine translation. Two of these models; NTM Style Attention and the Memory-Augmented Decoder extend the atten- tional encoder-decoder which has achieved state- of-the-art results on many language ... See full document
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Memory augmented Neural Machine Translation
... of neural models: the trans- lation function, represented by various neural net- works, is shared amongst all of the translation pairs, so high-frequency and low-frequency pairs impact each other by ... See full document
10
Cross Corpora Evaluation and Analysis of Grammatical Error Correction Models — Is Single Corpus Evaluation Enough?
... three neural machine translation (NMT)- based models (LSTM, CNN, and transformer) and a statistical machine translation (SMT)- based model is evaluated against six learner ... See full document
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Combining Translation Memory with Neural Machine Translation
... lation memory and Neural Machine Transla- tion (NMT) models, where we can select fi- nal translation outputs from either a translation memory or an NMT system, when the similar- ... See full document
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