Machine Translation in Pronunciation Space
Table 7: Performances of P2P-Tran, compared with T2T-Tran
['Direction', 'Category', 'Basic Units', 'Text Space Dev', 'Text Space Test', 'Pronunciation Space Dev', 'Pronunciation Space Test']
For P2P-Tran, we experiments two variants, namely, P2P-Tran-Syllable and P2P-Tran-Subword. In P2P-Tran-Syllable, the basic unit is Pinyin for Chinese and syllable for English. In P2P-Tran-Subword, the basic unit is subword for both Chinese and English. There are some important observations. First, the performances of all models are pretty good. This is probably because our dataset is large and the distributions of dev and test sets are similar to the distribution of train set. Second, the performance of P2P-Tran-Subword is consistently a little better than P2P-Tran-Syllable, which suggests that subwords are also very good basic units for translation in pronunciation space. Second, the performance of P2P-Tran-Subword is in general comparable to the performance of T2T-Tran. In Pronunciation space, P2P-Tran-Subword is slightly better than T2T-Tran (71.23 vs 71.06) in the direction of Zh-En, and T2T-Tran is slightly better than P2P-Tran-Subword (51.02 vs 50.90) in the direction of En-Zh. In Text space, in both directions, T2T-Tran seems to be a little better than P2P-Tran-Subword (43.46 vs 42.86 and 37.13 vs 35.64). However, there are errors in P2T-Conv, and when taking this into account, the differences will be subtle. Another important factor which also affects the performance of P2P-Tran is that a pronunciation sentence usually contains less information than its textual counterpart.