[PDF] Top 20 Multi agent Learning for Neural Machine Translation
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Multi agent Learning for Neural Machine Translation
... Conventional Neural Machine Translation (NMT) models benefit from the training with an additional agent, ...dual learning, and bidirectional decoding with one agent decod- ing ... See full document
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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
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Learning to Translate in Real time with Neural Machine Translation
... and learning the ...& Agent Settings We pre-trained the NMT environments for both language pairs and both directions following the same setting from (Cho and Esipova, ...an agent that starts ... See full document
10
Meta Learning for Low Resource Neural Machine Translation
... 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
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Transfer Learning for Low Resource Neural Machine Translation
... transfer learning and explains how we use it to im- prove machine translation ...the learning curves of transfer and no-transfer models, showing that transfer solves an overfitting problem, ... See full document
8
Active Learning for Interactive Neural Machine Translation of Data Streams
... active learning techniques to the translation of unbounded data streams via interactive neural machine ...the neural machine translation ...a neural machine ... See full document
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Multi Granularity Self Attention for Neural Machine Translation
... state-of-the-art neural machine trans- lation (NMT) uses a deep multi-head self- attention network with no explicit phrase in- ...tical machine translation has shown that ex- tending ... See full document
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Imitation Learning for Non Autoregressive Neural Machine Translation
... is thus significantly boosted. However, it comes at the cost that the translation quality is largely sacrificed since the intrinsic dependency within the natural language sentence is abandoned. A bulk of work has ... See full document
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Competence based Curriculum Learning for Neural Machine Translation
... large neural networks that are not only slow to train, but also often require many heuristics and optimization tricks, such as specialized learning rate schedules and large batch ...curriculum ... See full document
11
Using Target side Monolingual Data for Neural Machine Translation through Multi task Learning
... years, neural encoder-decoder models (Kalchbrenner and Blunsom, 2013; Sutskever et ...Statistical Machine Transla- tion (SMT) (Bojar et ...fied neural sequence-to-sequence model with ... See full document
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Adaptive Knowledge Sharing in Multi Task Learning: Improving Low Resource Neural Machine Translation
... Neural Machine Translation (NMT) is no- torious for its need for large amounts of bilingual ...is Multi- Task Learning (MTL) to leverage differ- ent linguistic resources as a source of ... See full document
6
Curriculum Learning and Minibatch Bucketing in Neural Machine Translation
... final translation quality and/or reduce the training time of an NMT system by organizing minibatches in two particular ...of translation quality and discusses the ... See full document
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Extending hybrid word character neural machine translation with multi task learning of morphological analysis
... tion into the target language surface form, while the auxiliary tasks consist of predicting the out- put of the FinnPos morphological analyzer for the target sentence. The auxiliary tasks provide addi- tional supervision ... See full document
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Learning to Stop in Structured Prediction for Neural Machine Translation
... 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
6
Curriculum Learning for Domain Adaptation in Neural Machine Translation
... For data-centric domain adaptation methods, our curriculum learning approach has connections to instance weighting. In our work, the presenta- tion of certain examples at specific training phases is equivalent to ... See full document
13
Multi Source Neural Machine Translation with Missing Data
... We describe the settings of common parts for all NMT models: multi-encoder NMT, mixture of NMT experts, and one-to-one NMT. We used global attention and attention feeding (Luong et al., 2015) for the NMT models ... See full document
8
Improving Robustness of Neural Machine Translation with Multi task Learning
... source text but also translate it. We design a strat- egy for synthesizing data triplets for this architec- ture. Our model could be viewed as a combina- tion of denoising source text and domain adap- tation, both of ... See full document
7
Semi Supervised Learning for Neural Machine Translation
... end-to-end neural machine transla- tion (NMT) has made remarkable progress recently, NMT systems only rely on par- allel corpora for parameter ...target-to-source translation models serve as the ... See full document
10
Zero Resource Translation with Multi Lingual Neural Machine Translation
... any multi-way parallel ...the translation quality (mea- sured in BLEU) by 3 points in the case of the test set (compare Table 2 (a–b) and Table 3 ... See full document
10
Exploiting Linguistic Resources for Neural Machine Translation Using Multi task Learning
... formance is achieved by using a complicated com- bination of several statistical models, which are in- dividually trained. For example, POS information was shown to be very helpful to model word re- ordering between ... See full document
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