[PDF] Top 20 Recurrent Neural Network based Tuple Sequence Model for Machine Translation

... deep neural network translation models because they can capture arbitrary-length context potentially, which are proven to estimate more accurate proba- bilities of bilingual tuples; (ii) we extend ... See full document

... German. Translation models are estimated on 102m words of parallel data for French-English, 91m words for German-English and English-German; be- tween ...is based on a large newswire corpus released as part ... See full document

... distribution based on translation probabilities to train the BN- NJM ...the translation results over the NNJM on Chinese- to-English and French-to-English ... See full document

... stochastic neural network model named Attention based Recurrent Temporal Restricted Boltzmann Machine (ARTRBM) is proposed for the poor performance of the traditional HRRP ... See full document

... QE model comes from the insufficiency of QE datasets to train the whole QE ...QE model is divided into two parts, and then different training data are used to train each of the separated parts: large-scale ... See full document

... the neural network training, the vocabulary size is limited to 30K high frequent words for both source and target ...All model parameters are initial- ized randomly with Gaussian distribution (Glorot ... See full document

... (2017). Based on the performance on the ja-en dev set we decode the plain text systems with a beam size of 4 and OSNMT with a beam size of 8 using our SGNMT decoder (Stahlberg et ... See full document

... to machine translation pre-reordering, we pro- pose an extension, denoted as Fragment RNN- RM, which includes reordering fragment fea- tures, at expense of a significant increase of time ... See full document

... Yaser Al-Onaizan and Kishore Papineni. 2006. Dis- tortion models for statistical machine translation. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual ... See full document

... Language Model (LM) usually performs better in Statistical Machine Translation (SMT), how to con- struct efficient large LM is an important topic in ...novel neural network based ... See full document

... Transformer translation systems (Vaswani et al., 2017), without recurrent and convolutional neural networks, rely on a positional embedding (PE) approach to encode order information into the input ... See full document

... French-to-English translation task, and the tech- nique has also proven effective in translation tasks between other European language pairs (Luong et ...are based on sequential ...of ... See full document

... language model needs to estimate 64K 2 bigrams, 64K 3 trigrams and 64K 4 ...statistical machine translation systems, especially smoothing techniques (Chen and Goodman, 1996), class n-gram language ... See full document

... the translation model, which breaks the limita- tion that the traditional encoder-decoder structure, such as Seq2Seq model, relies on a fixed length vector in the process of ...input sequence ... See full document

... Many metrics have been proposed for MT eval- uation. Earlier popular metrics are based on n- gram counts (e.g. BLEU (Papineni et al., 2002) and NIST (Doddington, 2002)) or word error rate. Other popular metrics ... See full document

... tention based NMT, the next word to attend is highly dependent on the previous ...a recurrent neural network to summarize the pre- ceding attentions which could impact the attention of the ... See full document

... Table 3 summarizes the main results of our experiment. In Table 3, “SRC tokens / sec” indicates the number of source tokens processed in one second. This is a standard measure for evaluating training speed; it is also ... See full document

... recursive neural network and recurrent neural network, and in turn integrates their respective capabilities: (1) new information can be used to generate the next hidden state, like ... See full document

... In this paper we have described a method for gener- ating abstractive compressions of scene description using attention-based bidirectional LSTMs (aRNN) trained on a new large dataset created from paired long and ... See full document

... Our model will contribute to further research on the use of RNN in the language translated for ...language model based on GRU-CNN-LSTM designed to treat textual data as dimensional inputs to predict ... See full document