[PDF] Top 20 Reinforcement Learning based Curriculum Optimization for Neural Machine Translation

... Figure 3 shows a coarse visualization of the hand- optimized policy of Wang et al. (2018), adapted to our 6-bin scenario, compared to the Q-learning policy on the same scenario. The former, by de- sign, telescopes ... See full document

... of curriculum learning ...samples based on their scores ...cause neural methods benefit from randomization in the minibatches and multiple ...probabilistic curriculum (Bengio et ...for ... See full document

... 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

... 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 ...gradient based method ... See full document

... noun translation by computing pronoun accuracy based on alignment of hypothesized translations with the ...Transformer- based context-aware models to do anaphora reso- ... See full document

... novel learning approach for RL- based NMT models, which integrates into the policy gradi- ent with an adequacy-oriented reward designed specifically for ...of reinforcement learn- ing, as well as a ... See full document

... 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

... length curriculum is particularly ...its learning while the more varied data better allow to learn to predict output length based on the input ... See full document

... riculum learning for NMT, although other related works have met with mixed ...several curriculum heuristics on training a translation system for a sin- gle epoch, presenting the training examples in ... See full document

... A natural extension of previous discussions is to combine both the source-side and target-side mono- lingual data for RL training. We consider two com- binations, the sequential method and the unified method. The former ... See full document

... 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

... ples of the model outputs are presented in Table 2 and Table 3. Some situations are successfully corrected using transformer (Table 2), whereas it failed to perform in other situations (Table 3). The reason for ... See full document

... the translation probability depending on an intermediate compu- tation of an alignment ...e.g. neural models, count-based models, ...the translation score is based on the alignment ... See full document

... Conventional Neural Machine Translation (NMT) models benefit from the training with an additional agent, ...dual learning, and bidirectional decoding with one agent decod- ing from left to ... See full document

... Table 2 and 3 summarize the experimental results. To avoid the affect of segmentation errors, the per- formance were evaluated by character-level BLEU (Papineni et al., 2002). We compare our proposed models (i.e., Forest ... See full document

... char- based approach provides quite better results than the proposed byte-based system but also it is worth mentioning that when using bytes, the results con- verged several hundred training iterations ... See full document

... Input data is collected via a survey conducted online for the people from major cities of India. The aim of these survey is to perform 2 tests on the students. First test is a behaviour test which consist of 40 general ... See full document

... fuzzy neural network (FNN) to guide the joint optimization of coverage and ...cooperative learning by sharing the opti- mization experience of SON ...fuzzy neural network, both fuzzy inference ... See full document

... A translation memory (TM) is proved to be helpful to improve neural machine translation (NMT). Existing ap- proaches either pursue the decoding efficiency by merely ac- cessing local ... See full document

... and optimization of corn combine harvester frame using modal analysis method’, Nongye Gongcheng Xuebao, 2015, 31, (19), ...‘Parameters optimization and separation performance of cylinder screen of combine ... See full document