[PDF] Top 20 Exploiting Sentential Context for Neural Machine Translation
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Exploiting Sentential Context for Neural Machine Translation
... side context, which are summarized into a senten- tial context ...source-side context vector is fed to the decoder, so that translation at each step is conditioned on the whole source-side ... See full document
7
Context Aware Monolingual Repair for Neural Machine Translation
... in context, these translations may end up being inconsistent with each ...in context of each ...the translation of several contex- tual phenomena in an English→Russian trans- lation task, as well as ... See full document
10
Context aware Neural Machine Translation with Coreference Information
... To avoid the problem, we propose a model that can effectively capture contextual informa- tion, preceding and succeeding sentences of the source sentence to be translated, by constructing an encoder that is based on ... See full document
6
Document Context Neural Machine Translation with Memory Networks
... We resort to manual evaluation as there is no standard metric which evaluates document-level discourse information like consistency or pronom- inal anaphora. By manual inspection, we observe that our models can identify ... See full document
10
Selective Attention for Context aware Neural Machine Translation
... correct translation of the noun “thoughts” (highlighted in bold). The context sentences shown in the bottom box had the highest attention weights as assigned by ...the context sentences, it seems ... See full document
11
Exploiting Multilingualism through Multistage Fine Tuning for Low Resource Neural Machine Translation
... contrast, exploiting the multi- parallel corpus through mixed pre-training (#6) or mixed fine-tuning (#7) brought consistent im- provements over the corresponding 1-to-2 models (#3 and ... See full document
7
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 ...global context for document- level neural ... See full document
10
Improving Back Translation with Uncertainty based Confidence Estimation
... in exploiting abundant monolingual cor- pora to improve low-resource neural machine translation (NMT), the synthetic bilingual cor- pora generated by NMT models trained on limited authentic ... See full document
12
When and Why is Document level Context Useful in Neural Machine Translation?
... For a fair evaluation of document-level NMT methods, we argue that one should make a sentence-level NMT baseline as strong as possi- ble first, i.e. by using more data or applying pro- per regularization. This will get ... See full document
11
Exploiting Linguistic Resources for Neural Machine Translation Using Multi task Learning
... many neural MT sys- tems is that they do not translate parts of the source sentence, or that parts of the source sentence are translated ...the translation that ex- actly matches the reference the ... See full document
10
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... the translation also depends on the professions from the Occupations test and its predicted ...the context of “him”, so we focus the analysis on the context of ... See full document
8
Context Aware Neural Machine Translation Learns Anaphora Resolution
... statistical machine transla- tion being largely supplanted with neural machine translation (NMT) models trained in an end-to- end fashion, an alternative is to directly provide additional ... See full document
11
The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2017
... attention neural machine translation model can be benefit by constraining the attentional process to adequately cover the source words (Sankaran et ...attentional neural net- work. Our ... See full document
8
Data augmentation using back translation for context aware neural machine translation
... of context-aware models is more affected by the lack of the training data than sentence-level NMT models, we investigated the impact of large-scale parallel data on the trans- lation quality of ... See full document
10
PARABANK: Monolingual Bitext Generation and Sentential Paraphrasing via Lexically-Constrained Neural Machine Translation
... through machine translation. This ap- proach trains a neural machine translation (NMT) model from a non-English source language to English over the en- tire bitext (Czech-English) ... See full document
8
Exploiting Semantics in Neural Machine Translation with Graph Convolutional Networks
... We parsed the English partitions of these datasets with a syntactic dependency parser (An- dor et al., 2016) and dependency-based seman- tic role labeler (Marcheggiani et al., 2017). We constructed the English vocabulary ... See full document
7
Exploiting Deep Representations for Neural Machine Translation
... Dataset. To compare with the results reported by previous work (Gehring et al., 2017; Vaswani et al., 2017; Hassan et al., 2018), we conducted experiments on both Chinese⇒English (Zh⇒En) and English⇒German (En⇒De) ... See full document
10
Neural Machine Translation with Extended Context
... larger translation units. Here, the neural network produces a translation of the entire ex- tended ...External context is not marked with specific prefixes anymore and token represen- tations ... See full document
11
Context Gates for Neural Machine Translation
... Figure 2(b) shows the results of manual evalu- ation on 200 source sentences randomly sampled from the test sets. Reducing the effect of source con- text (i.e., (0.8, 1.0) and (0.5, 1.0)) leads to more flu- ent yet less ... See full document
14
NICT’s participation to WAT 2019: Multilingualism and Multi step Fine Tuning for Low Resource NMT
... Neural machine translation (NMT) (Cho et ...alignments, translation rules, and complicated decoding algorithms, which are the characteris- tics of phrase-based statistical machine ... See full document
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