[PDF] Top 20 A Study of Reinforcement Learning for Neural Machine Translation
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A Study of Reinforcement Learning for Neural Machine Translation
... SOTA translation quality in several oth- er datasets. For En-De translation, we utilize the transformer base v1 ...En-Zh translation to be ...initial learning rate is 0.1, and we fol- low the ... See full document
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Reinforcement Learning based Curriculum Optimization for Neural Machine Translation
... initial study on meta-learning an NMT ...a reinforcement-learning (RL) approach involving a learned agent whose task is to choose a corpus bin, representing a given noise level, at each NMT ... See full document
8
Reinforcement Learning for Bandit Neural Machine Translation with Simulated Human Feedback
... Machine translation is a natural candidate problem for reinforcement learning from human feedback: users provide quick, dirty ratings on candidate translations to guide a system to ...current ... See full document
11
An Empirical Study of Language Relatedness for Transfer Learning in Neural Machine Translation
... empirical study of language relatedness for transfer learning in Neu- ral Machine ...transfer learning done on a X-Y language pair to Z-Y language pair has maximum impact when Z-Y is resource ... See full document
5
Imitation Learning for Non Autoregressive Neural Machine Translation
... 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
9
Active Learning for Interactive Neural Machine Translation of Data Streams
... We want to explore several lines of work in a future. First, we intend to apply our method to other datasets, involving linguistically diverse lan- guage pairs and low-resource scenarios, in order to observe whether the ... See full document
10
Interactive Predictive Neural Machine Translation through Reinforcement and Imitation
... imitation learning (IL) (Ross et al., 2011) and reinforcement learning (RL) (Sutton and Barto, 2018), respectively, us- ing only limited human ...active learning (Settles and Craven, ... See full document
11
Learning to Stop in Structured Prediction for Neural Machine Translation
... as reinforcement learning-based methods (Ran- zato et ...English translation, it also brings a different set of problems as partially discussed in (Ma, 2018) which we present with details ... See full document
6
Equalizing Gender Bias in Neural Machine Translation with Word Embeddings Techniques
... We did our study in the domain of news articles and professions. However, human corpora has a broad spectrum of categories, as an instance: in- dustrial, medical, legal that may rise other biases particular to ... See full document
8
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 ...deep reinforcement learning framework (Yue et ...active learning ... See full document
11
The UMD Neural Machine Translation Systems at WMT17 Bandit Learning Task
... land machine translation systems submit- ted to the WMT17 German-English Ban- dit Learning ...a translation system to a new domain, using only bandit feedback: the system receives a German ... See full document
7
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
10
Neural Machine Translation with Adequacy-Oriented Learning
... Although Neural Machine Translation (NMT) models have advanced state-of-the-art performance in machine translation, they face problems like the inadequate ...real translation ... See full document
8
Multi agent Learning for Neural Machine Translation
... diverse translation quality, in particu- lar Rel with refined position encoding achieves the best ...multi-agent learning is dependent on the performance of the co-trained agent ...multi-agent ... See full document
10
Assessment of Linearity Improvement in Optical Communication Systems with Machine Learning Methods
... experimental study is presented for the mitigation of nonlinear impairments by comparing Reinforcement Learning (RL) based machine learning method with Support Vector Machine ... See full document
7
Transfer Learning for Low Resource Neural Machine Translation
... 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
8
Meta Learning for Low Resource Neural Machine Translation
... Resource Translation NMT is known to easily over-fit and result in an inferior performance when the training data is limited (Koehn and Knowles, ...resource translation: (1) utilizing the resource of ... See full document
10
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
Ensemble Learning for Multi Source Neural Machine Translation
... the translation experiments the beam decoding size was set to ...ensemble translation, we are interested in how diverse the translation systems are in their ... See full document
10
Curriculum Learning for Domain Adaptation in Neural Machine Translation
... The total amount of the in-domain data in each domain is summarized in Table 3. In this paper, we uniformly sample 15k in-domain data from each dataset. We choose the amount of 15k, which makes up a relatively small ... See full document
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