[PDF] Top 20 Forest Based Neural Machine Translation

... tree-structured neural network (Eriguchi et ...the translation, which potentially introduces translation mistakes due to parsing errors (Quirk and Corston-Oliver, ...SMT, forest-based ... See full document

... IBM Models (Brown et al., 1993) depict- ed the word reordering knowledge as position- al relations between source and target word- s. Koehn et al. (2003) proposed a distortion model for phrase-based SMT ... See full document

... a translation system for a sin- gle epoch, presenting the training examples in an easy-to-hard order based on sentence length and vocabulary ...case), based on various difficulty ... See full document

... We compare our NMT systems in the English- German task with various other systems. These include the winning system in WMT’14 (Buck et al., 2014), a phrase-based system whose language models were trained on a huge ... See full document

... guages, attention is designed to find relevant con- text for predicting the next target word. Source words with high attention weights are not neces- sarily translation equivalents of the target word. Although ... See full document

... 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

... The goal of this work is to combine the advantages of neural and phrase-based machine translation systems. Handling of rare words is an essential aspect to consider when it comes to real-world ... See full document

... unsupervised machine translation track of the WMT’19 news shared task from German to ...tistical machine translation (PBSMT) model and a pre-trained language model to combine word-level ... See full document

... Tu et al. (2016) design a coverage vector for the translation history and then integrates it into the attention model. Similarly, Meng et al. (2016) maintain a tag vector to keep track of the attention history and ... See full document

... As models mentioned above only use 1-best segmentation as inputs, lattice which can pack many different segmentations in a compact form has been widely used in statistical machine translation (SMT) (Xu et ... See full document

... Back-translation — data augmentation by translating target monolingual data — is a crucial component in modern neural machine translation (NMT). In this work, we refor- mulate ... See full document

... and Neural Machine Transla- tion (NMT) models, where we can select fi- nal translation outputs from either a translation memory or an NMT system, when the similar- ity score of a test source ... See full document

... The method we proposed focuses on training process only. There is no restriction for the neural network structure. It can be used in any data paral- lelism framework and then distributed onto multi- GPUs. Also, ... See full document

... statistical machine transla- tion (SMT) that typically has a huge phrase/rule ta- ...and machine transla- tion community (Kalchbrenner and Blunsom, 2013; Cho et ...a neural decoder gen- erates the ... See full document

... We use the Transformer model (Vaswani et al., 2017) implemented in PyTorch in the fairseq-py toolkit (Edunov et al., 2017). All experiments are based on the “big” transformer model with 6 blocks in the encoder and ... See full document

... Chunk-level Evaluation To confirm that our models can capture local (intra-chunk) and global (inter-chunk) word orders well, we evaluated the translation quality at the chunk level. First, we performed ... See full document

... There are several previous studies on NMT using CNNs (Kalchbrenner and Blunsom, 2013; Cho et al., 2014b; Lamb and Xie, 2016; Kalchbrenner et al., 2016). Their models consist of serially connected multi-layer CNNs for en- ... See full document

... Lapata, 2008; Woodsend et al., 2010). These ap- proaches are described in more detail in Section 6. We instead explore a fully data-driven approach for generating abstractive summaries. Inspired by the recent success of ... See full document

... dependency-based translation systems suffer from a major drawback: they only use 1-best dependency trees for rule extraction, dependency language model training, and decod- ing, which potentially introduces ... See full document

... three neural machine translation (NMT)- based models (LSTM, CNN, and transformer) and a statistical machine translation (SMT)- based model is evaluated against six learner ... See full document