[PDF] Top 20 A Binarized Neural Network Joint Model for Machine Translation
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A Binarized Neural Network Joint Model for Machine Translation
... a neural network joint model (NNJM), which augments the n-gram neural network language model (NNLM) with an m-word source context window, as shown in Figure ...this ... See full document
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Learning Joint Multilingual Sentence Representations with Neural Machine Translation
... The use of multiple encoders and decoders was first studied in the context of neural MT. Dong et al. (2015) used multiple decoders, i.e. 1:N training, to achieve improved NMT performance. Zoph and Knight (2016) ... See full document
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A Recursive Recurrent Neural Network for Statistical Machine Translation
... the model from the root of the decoding tree and perform back propaga- tion along the tree ...final translation results may be not suitable for model ...the model using the final ... See full document
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A Convolutional Encoder Model for Neural Machine Translation
... to neural machine translation relies on bi-directional LSTMs to encode the source ...Romanian translation we achieve compet- itive accuracy to the state-of-the-art and on WMT’15 English-German ... See full document
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Abstractive Compression of Captions with Attentive Recurrent Neural Networks
... in neural machine transla- tion, we select a lower number of units than in this earlier work, namely 512 instead of 1024 (Sutskever et ...the network to prevent ...aRNN model on ... See full document
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Robust Neural Machine Translation with Joint Textual and Phonetic Embedding
... In Figure 3, we compare the performance of the baseline model and our models with β = 0.2, 0.4, 0.6, 0.8, 0.95, 1.0, respectively, on NIST06 test set and the two created noisy sets. The models are chosen based on ... See full document
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Neural Network Based Bilingual Language Model Growing for Statistical Machine Translation
... Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertol- di, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexan- dra Constantin, and Evan ... See full document
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Hybrid Neural Network Alignment and Lexicon Model in Direct HMM for Statistical Machine Translation
... ment direction is from target to source positions. This specific property allows us to introduce de- pendencies into the translation model that take the full source sentence into account. This as- pect will ... See full document
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Evaluating Layers of Representation in Neural Machine Translation on Part of Speech and Semantic Tagging Tasks
... One observation that has been made is that lower layers in the neural MT network learn different kinds of information than higher lay- ers. For example, Shi et al. (2016) and Belinkov et al. (2017) found ... See full document
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An Operation Sequence Model for Explainable Neural Machine Translation
... We evaluate on three language pairs: Japanese- English (ja-en), Spanish-English (es-en), and Portuguese-English (pt-en). We use the ASPEC corpus (Nakazawa et al., 2016) for ja-en and the health science portion of the ... See full document
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One Sentence One Model for Neural Machine Translation
... Neural machine translation (NMT) becomes a new state of the art and achieves promising translation performance using a simple encoder-decoder neural ...This neural network ... See full document
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Improving Sequence to Sequence Neural Machine Translation by Utilizing Syntactic Dependency Information
... the model with an encoder-decoder neu- ral network and using dependencies in which the input of the source language is in sequence form and the output of the target language will be gen- erated in a ... See full document
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A Neural Attention Model for Abstractive Sentence Summarization
... of neural machine translation, we combine a neural language model with a con- textual input ...generation model are trained jointly on the sentence summarization task. The ... See full document
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SHEF LIUM NN: Sentence level Quality Estimation with Neural Network Features
... language model (CSLM) to extract sentence embeddings and cross-entropy scores, (ii) a neural net- work machine translation (NMT) model, (iii) a set of QuEst features, and (iv) a com- ... See full document
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Beyond Weight Tying: Learning Joint Input Output Embeddings for Neural Machine Translation
... our model are essential and complementary, as well as, that their combination outperforms all previous out- put layers including those of bilinear input-output embedding ...the model to sampling- based ... See full document
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Recurrent Neural Network based Rule Sequence Model for Statistical Machine Translation
... previous translation rule using one-hot cod- ing, the output layer produces a probability distribu- tion over all translation rules, and the hidden layer maintains a representation of rule derivation ... See full document
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Found in Translation: Learning Robust Joint Representations by Cyclic Translations between Modalities
... learning joint distributions between two or more modalities (Donahue, Kr¨ahenb¨uhl, and Darrell 2016; Li et ...either joint (Pham et ...cyclic translation loss on modality ...jointly model the ... See full document
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Neural Hidden Markov Model for Machine Translation
... The neural HMM has been successfully applied in the literature on top of conventional phrase- based systems (Wang et ...the model is used to gener- ate and score candidates without assistance from a ... See full document
6
Reference Network for Neural Machine Translation
... NMT (Cheng et al., 2018) have a deep architec- ture with no less than 4 layers while Robust NMT introduces a additional discriminator for adversar- ial training. From the table, we can observe that our strong baseline ... See full document
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An Empirical Evaluation of Noise Contrastive Estimation for the Neural Network Joint Model of Translation
... The neural network joint model of transla- tion or NNJM (Devlin et al., 2014) combines source and target context to produce a power- ful translation feature. However, its softmax layer ... See full document
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