[PDF] Top 20 Modeling Coverage for Neural Machine Translation
Has 10000 "Modeling Coverage for Neural Machine Translation" found on our website. Below are the top 20 most common "Modeling Coverage for Neural Machine Translation".
Modeling Coverage for Neural Machine Translation
... the coverage mecha- nism in SMT, we propose a coverage-based ap- proach to NMT to alleviate the over-translation and under-translation ... See full document
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Simple and Effective Noisy Channel Modeling for Neural Machine Translation
... on neural noisy channel mod- eling relied on latent variable models that in- crementally process the source and target sen- ...with neural language models trained on bil- lions of words show that noisy ... See full document
6
A Simple and Effective Approach to Coverage Aware Neural Machine Translation
... Yonghui Wu, Mike Schuster, Zhifeng Chen, Quoc V. Le, Mohammad Norouzi, Wolfgang Macherey, Maxim Krikun, Yuan Cao, Qin Gao, Klaus Macherey, Jeff Klingner, Apurva Shah, Melvin Johnson, Xiaobing Liu, Lukasz Kaiser, Stephan ... See full document
6
Modeling Target Side Inflection in Neural Machine Translation
... contains translation errors, which may affect trans- lation ...language modeling data, but the “inverse” NMT system is not able to translate unseen target word forms (no lemmatization is done) and therefore ... See full document
11
Residual Stacking of RNNs for Neural Machine Translation
... language modeling task with a lim- ited parameter ...Recurrent Neural Networks (van den Oord et ...of neural nets with two-dimensional recurrent neural nets using residual ...the neural ... See full document
7
Variational Neural Machine Translation
... Chinese-English Translation Table 1 summarizes the BLEU scores of different systems on the Chinese-English translation ...improves translation quality in terms of BLEU on most cases, and ob- tains ... See full document
10
Incorporating Discrete Translation Lexicons into Neural Machine Translation
... the translation process, with results shown in Table ...broad coverage, did not sufficiently cover target-domain words (cov- erage of unique words in the source vocabulary was ... See full document
11
Hierarchical Modeling of Global Context for Document Level Neural Machine Translation
... Document-level machine translation (MT) re- mains challenging due to the difficulty in effi- ciently using document context for ...level neural machine translation ...document-level ... See full document
10
Modeling Source Syntax for Neural Machine Translation
... Shen et al., 2008; Li et al., 2013). While it is yet to be seen how syntax can benefit NMT effectively, we find that translations of NMT sometimes fail to well respect source syntax. Figure 1 (a) shows a ... See full document
10
Coverage Embedding Models for Neural Machine Translation
... special coverage embedding vec- tor for each source word to start with, and keeps up- dating those coverage embeddings with neural net- works as the translation ... See full document
6
Modeling Coherence for Discourse Neural Machine Translation
... speech translation task with TED talks (Cettolo, Girardi, and Federico 2012) as training corpus, which includes multiple entire ...sentence translation quality by ... See full document
8
Neural Machine Translation with Recurrent Attention Modeling
... We compare our results with our own baseline and with results from related works if the experimental setting are the same. From Table 2, we can see that adding dependency improves RNNSearch model by 0.5 and 0.7 on ... See full document
5
UCSYNLP Lab Machine Translation Systems for WAT 2019
... Myanmar Translation. In Myanmar to English translation, word- based NMT model outperforms Myanmar Syllable-based NMT model in terms of BLEU score and the RIBES ... See full document
5
Proceedings of the Human Informed Translation and Interpreting Technology Workshop (HiT IT 2019)
... Human-Informed Translation and Interpreting Technology (HiT-IT 2019) took place in Varna, Bulgaria and spanned over two days (5-6 September 2019), as a post-RANLP 2019 conference ... See full document
10
Combining Translation Memory with Neural Machine Translation
... a translation memory to translate known sentences and phrase, while still allowing a more flexible Machine Translation (MT) model to translate less- familiar phrases and sentences without sacrific- ... See full document
8
Iterative Back Translation for Neural Machine Translation
... NMT is a data-hungry approach, requiring a large amount of parallel data to reach reasonable per- formance (Koehn and Knowles, 2017). In a low- resource setting, only small amount of parallel data exist. Previous work ... See full document
7
Guiding Neural Machine Translation with Retrieved Translation Pieces
... Considering the computational complexity, their method also performs search engine retrieval for each input sentence and computes the edit dis- tance between the input sentence and the retrieved source sentences as our ... See full document
11
Graph Based Translation Memory for Neural Machine Translation
... One main reason is that P-TFM is highly sensitive to the value of the reward defined in Zhang et al. (2018), which is determined by the sentence similarity and the hyperparame- ter. To further support this, we add the ... See full document
8
Generalizing Back Translation in Neural Machine Translation
... English translation task, which allows us to directly compare the properties of synthetic and natural ...of translation quality, these do not result in consistent improvements over the typical beam search ... See full document
8
Machine Translation by Modeling Predicate Argument Structure Transformation
... Formally, PAS represents the main structure of a sentence. However, sometimes the sentence cannot be fully covered by a PAS, especially when there are several predicates in the sentence. In order to translate the whole ... See full document
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