[PDF] Top 20 Learning to Parse and Translate Improves Neural Machine Translation

... Neural Machine Translation (NMT) has enjoyed impressive success without relying on much, if any, prior linguistic knowledge. Some of the most recent studies have for instance demonstrated that NMT ... See full document

... deep learning have prompted a surge of interest in the applica- tion of multitask and transfer learning to NLP ...multitask learning (MTL) to the identification of Multiword Expressions ...dency ... See full document

... simultaneous translation, ac- curacy of standard MT systems has greatly im- proved with the introduction of neural-network- based MT systems (NMT) (Sutskever et ...ous translation either through ... See full document

... active learning techniques to the translation of unbounded data streams via interactive neural machine ...interactively translate those ...the neural machine ... See full document

... resource neural machine translation ...low-resource translation as a meta- learning problem, and we learn to adapt to low-resource languages based on multilingual high-resource language ... See full document

... NMT Model Our baseline model consists of a 2-layer bi-directional LSTM encoder with an em- beddings size of 512 and a hidden size of 512. The 1-layer LSTM decoder with 512 hidden units uses an attention network with 128 ... See full document

... consistently improves as the number of agents increases, and we observe that 1) The four baseline systems with different implementa- tions present diverse translation quality, in particu- lar Rel with ... See full document

... that translate from different source languages into the same target ...for translation systems, which may range from the way particular words are translated to the way the whole sentence is ...that ... See full document

... Inadequate translation problem is a commonly-cited weakness of NMT models (Tu et ...adequacy learning at the word-level inside the genera- tor ...of translation candidates, which is calculated by ... See full document

... statistical machine trans- lation (SMT) to capture dependencies between distant words that impact morphological agree- ment, subcategorisation and word order (Galley et ...convolutional neural networks, ... See full document

... We focus on language pairs with small amounts of parallel data where monolingual data has the most impact. On the relatively low- resource language pairs of English↔Turkish and English ↔ Romanian, we find that our ... See full document

... The second subcategory includes insertions or deletions of auxiliary verbs, for which there were 15 improvements and not a single degradation. The reason why these errors occurred in the first place is that when a ... See full document

... better translation than source side scores, and bilingual criteria is somewhere in between, for all the sizes of Paracrawl data we experi- mented ...lum learning models ...bi improves the BLEU score ... See full document

... Bolukbasi et al. (2016) uses a set of words to define the gender direction and to neutralize and equalize the bias from the word vectors. Three set of words are used: One set of ten pairs of words such as woman-man, ... See full document

... We compare our model with seq2seq, BSO and seq2seq with length reward (Huang et al., 2017) which involves hyper-parameter to solve neural model’s tendency for shorter hypotheses (our pro- posed method does not ... See full document

... to decide which part of target sentence it will fo- cus on, but also to decide the correct target word of that part. All decisions are made by interac- tions with other decoding states. Delayed super- visions (correct ... See full document

... Neural Monkey is quite flexible in model con- figuration but we restrict our experiments to the standard encoder-decoder architecture with atten- tion as proposed by Bahdanau et al. (2015). We use the same model ... See full document

... develop machine translation ...a machine translation system by annotating first a small quantity of data, training a parser, training a system that uses the parses produced by that parser and ... See full document

... Transfer learning uses knowledge from a learned task to improve the performance on a related task, typically reducing the amount of required training data (Torrey and Shavlik, 2009; Pan and Yang, ...transfer ... See full document

... to 1 if its condition is satisfied and 0 otherwise. Next we need to decide how to aggregate the rel- ative word frequencies of all words in a sentence to obtain a single difficulty score for that sentence. Previous ... See full document