Machine Translation in Pronunciation Space
Table 5: P2P-Tran with different basic units. The numbers in bracket in first column are the number of basic units.
['Basic Units', 'Direction', 'Dev', 'Test']
We first compare different basic units in pronunciation space, on the task of P2P-Tran. Specially, we compare 3 types of P2P-Tran, namely, 1) Pinyins for Chinese and phonemes for English, 2) Pinyins for Chinese and syllables for English, and 3) subwords for Chinese and subwords for English. We trained the models for both Zh-En and En-Zh, and for fair comparison, the translations are first transformed into sequences of Pinyins for Chinese and sequences of phonemes for English, then the 4-gram BLEU score papineni2002bleu is calculated by multi-bleu.perl. Note that in this paper, all BLEU scores in the pronunciation space are calculated in this way, and all BLEU scores are single-reference scores. Consistent with the text translation, the subword approach (type 3 of P2P-Tran) achieves the best performance in both directions. The syllable approach (type 2 of P2P-Tran) is about 1 BLEU point worse. However, when the basic unit is switched to phonemes in English side, the performance is much worse, especially in the direction of Zh-En.