[PDF] Top 20 Incremental Decoding and Training Methods for Simultaneous Translation in Neural Machine Translation

... the neural MT architecture to oper- ate in an online fashion where i) the encoder and the attention are updated dynamically as new input words are added, through a READ operation, and ii) the decoder generates ... See full document

... two methods which use dynamic oracle to solve the issue mentioned ...during training time the truth have been completely altered, our method will always pro- vide the model with the best suitable token in ... See full document

... two decoding layers, shared source and target embeddings, and use the Sock- eye toolkit (Hieber et ...two methods we propose, train-by-appending and train-by-replace with the constrained decoding ... See full document

... the training set to improve the training pro- cess of phrase-based SMT and prune the phrase- based rule ...forced decoding, but they used a high penalty for all insertions and ...forced ... See full document

... statistical machine translation. Mini- mum error rate training (MERT) was performed on the development set (dev2010) to optimize the feature weights of the log-linear model used in ...During ... See full document

... On the other hand, architectures that share pa- rameters between all languages (Johnson et al., 2017) by using a single encoder and decoder trained to be able to translate from and to any of the languages of the system. ... See full document

... Most efforts in statistical machine translation so far are variants of either phrase-based or syntax-based models. From a theoretical point of view, phrase- based models are neither expressive nor ... See full document

... decade, translation as well as translator training have experienced a significant ...web-based translation resources, such as Google ...the translation didactic process and training is ... 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 ...In machine translation ... See full document

... A*) decoding algorithm is a kind of best-first search which was first intro- duced in the domain of speech recognition (Je- linek, ...stack decoding algorithm ... See full document

... 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

... Although NMT models have achieved results on par with or better than conventional SMT, they still suffer from a major drawback: the models are op- timized to maximize the likelihood of training data instead of ... See full document

... Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondrej Bojar, Alexandra Con- stantin, and Evan ... See full document

... 2: Translation accuracy and Relative decoding time for orthographic syllable level translation using different decoding methods and ...Relative decoding time is indicated as a ... See full document

... Table 2: Examples of Correct, Okay, and Incorrect Translations: for each translation, the first line is an input German sentence, the second line is the human made target translation for[r] ... See full document

... In this paper, we describe our submission to the WMT19 low-resource parallel corpus fil- tering shared task. Our main approach is based on the LASER toolkit (Language-Agnostic SEntence Representations), which uses an ... See full document

... The translation process is treated as a noisy chan- nel model, like those used in speech recognition in which there exists e transcribed as f, and a trans- lation is to infer the best e from f in terms of ...a ... See full document

... Already at (14), we explicitly ruled out translations y having no derivation at all in the hypergraph. However, suppose the hypergraph were very large (thanks to a large or smoothed translation model and weak ... See full document

... 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

... two decoding objectives for two ...evaluating translation systems) and log-likelihood (the most widely used learning criterion for neural machine ...greedy decoding and beam ...greedy ... See full document