Acknowledgements
Table 4.5: Direct Transfer performance with increasing the filtering limit. The best performance (BLEU) is in bold. The ‡ represents significantly better results when compared to baseline.
['Language pair', 'Baseline', 'Filter 100', 'Filter 500']
We increased the threshold from 100 to 500 tokens per sentence. This filtering is already high enough to maintain most of the training corpus. Specifically, it removes only 3.6% sentences from the Odia–English and 0.8% from Russian–English corpus. Increasing the number of tokens per sentence increases the nodes in the computation graph. In order to fit the same model on GPU, we run these experiments on our 16GB GPUs Quadro P5000. BiRNN Bidirectional Recurrent Neural Network BP Brevity Penalty BPE Byte Pair Encoding CNN Convolutional Neural Network IWSLT International Workshop on Spoken Language Translation LanideNN Language Identification by Neural Networks LM Language Model LSTM Long Short-Term Memory cells MT Machine Translation NLP Natural Language Processing NMT Neural Machine Translation NN Neural Network PBMT Phrase-Based Machine Translation POS Part-of-Speech OOV Out-of-Vocabulary RNN Recurrent Neural Network SMT Statistical Machine Translation T2T Tensor2tensor WAT Workshop on Asian Translation WMT Workshop on Statistical Machine Translation