In this table, ENG represents the results of <cite>Yu et al. (2018a)</cite> 's model on their English dataset but under our split. C-ML and C-S denote the results of our Chinese models based on characters with multi-lingual embeddings and monolingual embeddings, respectively, while WY-ML, WY-S denote the wordbased models applying YZ segmentor with multilingual embeddings and monolingual embeddings, respectively. First, compared to the best results of human translation (C-ML and WY-ML), machine translation results show a large disadvantage (e.g. 7.1% vs 12.1% using C-ML). Out of the 100 translated Second, comparisons among C-ML, WY-ML and WJ-ML, and among C-S, WY-S and WJ-S show that multi-lingual embeddings give superior results compared to monolingual embeddings, which is likely because they bring a better connection between natural language questions and database columns.