[PDF] Top 20 Dependency-Based Self-Attention for Transformer NMT
Has 10000 "Dependency-Based Self-Attention for Transformer NMT" found on our website. Below are the top 20 most common "Dependency-Based Self-Attention for Transformer NMT".
Dependency-Based Self-Attention for Transformer NMT
... the Transformer model (Vaswani et ...(RNN)- based models (Sutskever et ...garnered attention from MT re- searchers. The Transformer model computes the strength of a relationship between two ... See full document
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Design and Implementation of Consecutive Interpreting System Based on Transformer NMT Model
... multi-head self-attention mechanism, and the second is a simple, position-wise fully connected feed-forward ...multi-head attention over the output of the encoder stack ...in NMT, ... See full document
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Tilde’s Machine Translation Systems for WMT 2018
... when NMT systems first showed to achieve significantly bet- ter results than statistical machine translation (SMT) systems (Bojar et ...for NMT have changed on a yearly (and even more fre- quent) ...shallow ... See full document
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IITP MT System for Gujarati English News Translation Task at WMT 2019
... network based encoder-decoder NMT architecture (Cho et ...the self-attention to better en- code a sequences. Self-attention is used in the ar- chitecture to calculate ... See full document
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Attending to Future Tokens for Bidirectional Sequence Generation
... bidirectional self-attention module where every token can attend to every other ...a Transformer is not restricted to sequen- tial ...in Transformer or LSTM-based models (Gu et ... See full document
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How Much Attention Do You Need? A Granular Analysis of Neural Machine Translation Architectures
... and self-attention (Vaswani et ...the dependency between source language time steps, leading to considerable speed-ups in training time and improvements in ...The Transformer, however, ... See full document
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Knowledge Enriched Transformer for Emotion Detection in Textual Conversations
... Knowledge-Enriched Transformer (KET) to effectively incorporate con- textual information and external knowledge bases to address the aforementioned ...The Transformer (Vaswani et ...The ... See full document
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On the Relation between Position Information and Sentence Length in Neural Machine Translation
... CNN, Transformer allows us to change the po- sition information ...vanilla Transformer, the modified Trans- former using self-attention with relative positional encodings (Shaw et ...improves ... See full document
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Look Harder: A Neural Machine Translation Model with Hard Attention
... hard-attention based Transformer model and the original soft-attention based Transformer model indicates the effectiveness of selecting a few rele- vant source tokens for each ... See full document
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Tensorized Self Attention: Efficiently Modeling Pairwise and Global Dependencies Together
... tensorized self-attention (MTSA), for context ...pairwise dependency is captured by an efficient dot-product based token2token self- attention, while the global dependency ... See full document
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Joey NMT: A Minimalist NMT Toolkit for Novices
... Joey NMT, a minimalist neural machine translation toolkit based on PyTorch that is specifically designed for ...Joey NMT provides many popular NMT features in a small and simple code base, so ... See full document
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Dependency-Based Relative Positional Encoding for Transformer NMT
... dependency-based NMT model that uses depen- dency trees for both source and target ...the Transformer, but did not improve the Transformer’s ...the Transformer model so that it incorpo- ... See full document
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Lexically Constrained Decoding for Sequence Generation Using Grid Beam Search
... interaction, at each iteration we chose a phrase of up to three tokens from the reference transla- tion which does not appear in the current MT hy- potheses. In the strict setting, the complete phrase must be missing ... See full document
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Moon IME: Neural based Chinese Pinyin Aided Input Method with Customizable Association
... There are variable referential natural language processing studies(Cai et al., 2018; Li et al., 2018b; He et al., 2018; Li et al., 2018a; Zhang et al., 2018a; Cai et al., 2017a,b) for IME devel- opment to refer to. Most ... See full document
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A Multiscale Visualization of Attention in the Transformer Model
... When specific neurons are linked to a tangi- ble outcome, it presents an opportunity to inter- vene in the model (Bau et al., 2019). By altering the relevant neurons—or by modifying the model weights that determine these ... See full document
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Massive Exploration of Neural Machine Translation Architectures
... Table 6 shows the effect of varying beam widths and adding length normalization penalties. A beam width of 1 corresponds to greedy search. We found that a well-tuned beam search is crucial to achieving good results, and ... See full document
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Bilingual GAN: A Step Towards Parallel Text Generation
... space based GAN methods and atten- tion based sequence to sequence models have achieved impressive results in text generation and unsupervised machine translation respec- ...space based model capable ... See full document
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Analyzing the Structure of Attention in a Transformer Language Model
... The Transformer is a fully attention-based alternative to recurrent networks that has achieved state-of-the-art results across a range of NLP ...of attention in a Transformer language ... See full document
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Relation Classification Using Segment Level Attention based CNN and Dependency based RNN
... Traditional supervised approaches can be di- vided into feature-based methods and kernel meth- ods. Feature-based methods focus on extract- ing and combining relevant features. Rink and Harabagiu (2010) ... See full document
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The AMU UEDIN Submission to the WMT16 News Translation Task: Attention based NMT Models as Feature Functions in Phrase based SMT
... • N-best list extraction is more difficult, as hypotheses that have been recombined do not display correct cumulative sums for the NMT-feature scores. The one-best trans- lation is always correctly scored as it ... See full document
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