[PDF] Top 20 Dense Information Flow for Neural Machine Translation
Has 10000 "Dense Information Flow for Neural Machine Translation" found on our website. Below are the top 20 most common "Dense Information Flow for Neural Machine Translation".
Dense Information Flow for Neural Machine Translation
... Recently, neural machine translation has achieved remarkable progress by intro- ducing well-designed deep neural net- works into its encoder-decoder frame- ...allows dense connection in ... See full document
10
Leveraging Rule Based Machine Translation Knowledge for Under Resourced Neural Machine Translation Models
... We used OpenNMT (Klein et al., 2017), a generic deep learning framework mainly specialised in sequence-to-sequence models covering a variety of tasks such as machine translation, summarisa- tion, speech ... See full document
9
Improving Sequence to Sequence Neural Machine Translation by Utilizing Syntactic Dependency Information
... In this work, we proposed a method in which the Seq2Dep NMT model is trained by utilizing syn- tactic dependencies to provide the model more abundant information. In other words, Seq2Dep model learns the potential ... See full document
9
Boosting Neural Machine Translation
... We inspect the selected “difficult” examples ac- cording to perplexity. We find that almost all such examples containing complex structures, thus be- ing difficult to be translated. To force the system to pay much ... See full document
6
Graph Based Translation Memory for Neural Machine Translation
... based neural machine translation has been witnessed increasing ...the translation rules as SMT, many works resort to different ...a translation memory to fine tune the NMT model which ... See full document
8
Incorporating Discrete Translation Lexicons into Neural Machine Translation
... In this paper, we propose a simple, yet effective method to incorporate discrete, probabilistic lexi- cons as an additional information source in NMT (§3). First we demonstrate how to transform lexi- cal ... See full document
11
Guiding Neural Machine Translation with Retrieved Translation Pieces
... (Koehn et al., 2003), NMT has trouble with low- frequency words or phrases (Arthur et al., 2016; Kaiser et al., 2017), and also generalizing across domains (Koehn and Knowles, 2017). A num- ber of methods have been ... See full document
11
Retrieving Sequential Information for Non Autoregressive Neural Machine Translation
... the translation tasks, we apply sequence-level knowledge distillation to construct the distillation corpus where the target side of the training cor- pus is replaced by the output of an autoregres- sive ... See full document
12
Tensor2Tensor for Neural Machine Translation
... additional information per feature (for example, its type, vocabulary size, and an encoder able to convert samples to and from human and machine-readable ... See full document
7
On the Relation between Position Information and Sentence Length in Neural Machine Translation
... in neural machine translation ...Recurrent Neural Net- work (RNN)-based ...position information which is essential to process sequential ...position information type of NMT ... See full document
11
Pre Translation for Neural Machine Translation
... One main drawback of this approach is that the whole source sentence has to be stored in a fixed- size context vector. To overcome this problem, (Bahdanau et al., 2014) introduced the soft attention mechanism. Instead of ... See full document
9
Context aware Neural Machine Translation with Coreference Information
... The scores for Cor-g is always better than those for Cor-m. From this result, we can say that the gating mechanism in Cor-g works well. In ad- dition, as shown in Figure 3, the translation of Cor-g has a closer ... See full document
6
Neural Machine Translation of Logographic Language Using Sub character Level Information
... Neural machine translation (Cho et al., 2014) (NMT) systems based on sequence-to-sequence models (Sutskever et al., 2014) have recently be- come the de facto standard architecture. The models use ... See full document
9
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... proper translation has to be derived from ...the translation sys- tem is gender biased, the context is disregarded, while if the system is neutral, the translation is cor- rect (since it has the ... See full document
8
Paraphrasing Revisited with Neural Machine Translation
... of neural machine translation, a new approach to machine transla- tion based purely on neural networks (Kalchbren- ner and Blunsom, 2013; Bahdanau et ...deep neural network ... See full document
13
UCSYNLP Lab Machine Translation Systems for WAT 2019
... years, Neural Machine Translation (NMT) (Bahdanau et ...Statistical Machine Translation (SMT) ...the machine translation based on neural ... See full document
5
Residual Stacking of RNNs for Neural Machine Translation
... of machine translation is greatly improved by applying neural networks par- tially in a Statistical Machine Translation (SMT) pipeline (Zou et ...end-to-end neural network based ... See full document
7
Combining Translation Memory with Neural Machine Translation
... First of all, we evaluate the overall translation accuracy of each NMT system and production system on the concatenated data (ITEM+TEXT). Table 2 reports the case-sensitive sacreBLEU scores of the NMT systems and ... See full document
8
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
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 corpora ... See full document
6
Related subjects