[PDF] Top 20 Learning to Stop in Structured Prediction for Neural Machine Translation
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Learning to Stop in Structured Prediction for Neural Machine Translation
... in neural machine ...to stop during training, and the model naturally prefers longer hypotheses dur- ing the testing time in practice since they use the raw score instead of the probability-based ... See full document
6
Distilling Knowledge for Search based Structured Prediction
... and neural machine translation ...Is learning from distillation loss sta- ble?” are yet to be ...fully learning from the distillation loss by studying the effect of α in the ... See full document
10
Meta Learning for Low Resource Neural Machine Translation
... Transfer Learning We meta- learn the initial models on all the source tasks us- ing either Ro-En or Lv-En as a validation ...transfer learning strategy. As presented in Fig. 3, the proposed learning ... See full document
10
Bandit Structured Prediction for Neural Sequence to Sequence Learning
... lift structured pre- diction under bandit feedback from linear models to non-linear sequence-to-sequence learning us- ing recurrent neural networks with ...of neural machine ... See full document
11
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
... this learning framework has been com- bined with recurrent neural networks to solve ma- chine translation (Bahdanau et ...2016), neural architecture search (Zoph and Le, 2017), and device ... See full document
11
Learning to Parse and Translate Improves Neural Machine Translation
... of neural machine translation, which results in two separate models rather than a single end-to-end ...trained translation model strictly requires the availability of external tools during ... See full document
7
Ensemble Learning for Multi Source Neural Machine Translation
... for translation systems, which may range from the way particular words are translated to the way the whole sentence is ...that translation systems trained on language pairs with different source languages ... See full document
10
Watermarking the Outputs of Structured Prediction with an application in Statistical Machine Translation
... Machine learning algorithms provide structured results to input queries by simulating human be- ...automatic machine translation (Brown et ...output structured results are made ... See full document
10
Imitation Learning for Non Autoregressive Neural Machine Translation
... Deep neural network with autoregressive frame- work has achieved great success on machine trans- lation, with different choices of ...the prediction of each target token has to condition on ... See full document
9
A Structured Prediction Approach for Statistical Machine Translation
... a prediction of input x in domain X into output y in domain Y, like SVM and deci- sion trees, cannot keep the structure information during ...sensitive learning algorithm to learn ...and learning at ... See full document
6
Learning to Actively Learn Neural Machine Translation
... Meta-AL learning Several meta-AL ap- proaches have been proposed to learn the AL selection strategy automaticclay from ...reinforcement learning framework (Yue et ...model prediction function. Fang ... See full document
11
A Study of Reinforcement Learning for Neural Machine Translation
... a neural translation model with the objective grad- ually shifting from maximizing token-level likeli- hood to optimizing the sentence-level BLEU ...English-French translation but not on ... See full document
10
Neural Machine Translation with Adequacy-Oriented Learning
... Inadequate translation problem is a commonly-cited weakness of NMT models (Tu et ...a prediction network to estimate the future cost of translating the uncovered source ...adequacy learning at the ... See full document
8
A Coactive Learning View of Online Structured Prediction in Statistical Machine Translation
... online learning framework such as coactive learning covers prob- lems such as changing n-best lists after each up- date that were explicitly excluded from the batch analysis of Gimpel and Smith (2012) and ... See full document
11
NICT’s participation to WAT 2019: Multilingualism and Multi step Fine Tuning for Low Resource NMT
... fer learning and back-translation for our submis- ...transfer learning tech- niques such as fine-tuning can reliably improve translation quality especially for translation into ... See full document
5
Improving Japanese to English Neural Machine Translation by Voice Prediction
... NTCIR corpus. Herein, the highest accuracy is 66.0%, which is obtained by the model using three features, i.e., SrcSubj, SrcPred, and SrcVoice. Sr- cVoice is the best predictive feature because the voice concordance rate ... See full document
6
Competence based Curriculum Learning for Neural Machine Translation
... 2009). Machine learn- ing models are typically trained using stochastic gradient descent methods, by uniformly sampling mini-batches from the pool of training examples, and using them to compute updates for the ... See full document
11
The Karlsruhe Institute of Technology Systems for the News Translation Task in WMT 2017
... news translation with three directions: English-German, German-English and ...the neural attentional encoder- decoder model, which we enhanced with different features such as context gates for more ... See full document
8
Active Learning for Interactive Neural Machine Translation of Data Streams
... All these works were based on SMT systems. However, the recently introduced NMT paradigm (Sutskever et al., 2014; Bahdanau et al., 2015) has irrupted as the current state-of-the-art for MT (Bo- jar et al., 2017). Several ... See full document
10
Learning to Translate in Real time with Neural Machine Translation
... simultaneous machine translation in the scenario of real-time speech interpretation (F¨ugen et ...The translation model then works independently based on each of these segments, potentially limiting ... See full document
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