Acknowledgements
Table 4.15: The scores (BLEU) for cold-start methods (Direct Transfer and Transformed Vocabulary) with the warm-start method of Balanced Vocabulary.
['Child language pair', 'Direct Transfer', 'Transformed Voc.', 'Balanced Voc.']
We see that the warm-start method reaches in most cases significantly better performance in both directions. The only exception is the Russian→English and English→Estonian, where neither warm-start Balanced Vocabulary nor the Transformed Vocabulary is significantly better than the other. Hence warm-start should work for high-resource language pairs. However, the warm-start approach has one disadvantage over the cold-start: we cannot reuse any parent model, and we have to train the parent model for each child language pair separately. 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